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Learning from End User Data with Shuffled Differential Privacy over Kernel Densities | https://openreview.net/forum?id=QjSOgxJ0hp | [
"Tal Wagner"
] | Spotlight | We study a setting of collecting and learning from private data distributed across end users.
In the shuffled model of differential privacy, the end users partially protect their data locally before sharing it, and their data is also anonymized during its collection to enhance privacy.
This model has recently become a prominent alternative to central DP, which requires full trust in a central data curator, and local DP, where fully local data protection takes a steep toll on downstream accuracy.
Our main technical result is a shuffled DP protocol for privately estimating the kernel density function of a distributed dataset, with accuracy essentially matching central DP.
We use it to privately learn a classifier from the end user data, by learning a private density function per class.
Moreover, we show that the density function itself can recover the semantic content of its class, despite having been learned in the absence of any unprotected data.
Our experiments show the favorable downstream performance of our approach, and highlight key downstream considerations and trade-offs in a practical ML deployment of shuffled DP. | differential privacy, shuffled differential privacy, kernel density estimation, kde | We present a method for collecting and learning a classifier from private data distributed across end users, via kernel density estimates in the shuffled DP model. | 7,378 | 2502.14087 |
Biologically Constrained Barrel Cortex Model Integrates Whisker Inputs and Replicates Key Brain Network Dynamics | https://openreview.net/forum?id=UvfI4grcM7 | [
"Tianfang Zhu",
"Dongli Hu",
"Jiandong Zhou",
"Kai Du",
"Anan LI"
] | Spotlight | The brain's ability to transform sensory inputs into motor functions is central to neuroscience and crucial for the development of embodied intelligence. Sensory-motor integration involves complex neural circuits, diverse neuronal types, and intricate intercellular connections. Bridging the gap between biological realism and behavioral functionality presents a formidable challenge. In this study, we focus on the columnar structure of the superficial layers of mouse barrel cortex as a model system. We constructed a model comprising 4,218 neurons across 13 neuronal subtypes, with neural distribution and connection strengths constrained by anatomical experimental findings. A key innovation of our work is the development of an effective construction and training pipeline tailored for this biologically constrained model. Additionally, we converted an existing simulated whisker sweep dataset into a spiking-based format, enabling our network to be trained and tested on neural signals that more closely mimic those observed in biological systems. The results of object discrimination utilizing whisker signals demonstrate that our barrel cortex model, grounded in biological constraints, achieves a classification accuracy exceeds classical convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs), by an average of 8.6%, and is on par with recent spiking neural networks (SNNs) in performance. Interestingly, a whisker deprivation experiment, designed in accordance with neuroscience practices, further validates the perceptual capabilities of our model in behavioral tasks.
Critically, it offers significant biological interpretability: post-training analysis reveals that neurons within our model exhibit firing characteristics and distribution patterns similar to those observed in the actual neuronal systems of the barrel cortex. This study advances our understanding of neural processing in the barrel cortex and exemplifies how integrating detailed biological structures into neural network models can enhance both scientific inquiry and artificial intelligence applications. The code is available at https://github.com/fun0515/RSNN_bfd. | Barrel cortex, biophysical modeling, sensory-motor integration, recurrent spiking neural networks | Training a biologically constrained barrel cortex model and exploring its biological interpretability. | 7,299 | null |
FairMT-Bench: Benchmarking Fairness for Multi-turn Dialogue in Conversational LLMs | https://openreview.net/forum?id=RSGoXnS9GH | [
"Zhiting Fan",
"Ruizhe Chen",
"Tianxiang Hu",
"Zuozhu Liu"
] | Spotlight | The increasing deployment of large language model (LLM)-based chatbots has raised concerns regarding fairness. Fairness issues in LLMs may result in serious consequences, such as bias amplification, discrimination, and harm to minority groups. Many efforts are dedicated to evaluating and mitigating biases in LLMs. However, existing fairness benchmarks mainly focus on single-turn dialogues, while multi-turn scenarios, which better reflect real-world conversations, pose greater challenges due to conversational complexity and risk for bias accumulation. In this paper, we introduce a comprehensive benchmark for fairness of LLMs in multi-turn scenarios, **FairMT-Bench**. Specifically, We propose a task taxonomy to evaluate fairness of LLMs cross three stages: context understanding, interaction fairness, and fairness trade-offs, each comprising two tasks. To ensure coverage of diverse bias types and attributes, our multi-turn dialogue dataset FairMT-10K is constructed by integrating data from established fairness benchmarks. For evaluation, we employ GPT-4 along with bias classifiers like Llama-Guard-3, and human annotators to ensure robustness. Our experiments and analysis on FairMT-10K reveal that in multi-turn dialogue scenarios, LLMs are more prone to generating biased responses, showing significant variation in performance across different tasks and models. Based on these findings, we develop a more challenging dataset, FairMT-1K, and test 15 current state-of-the-art (SOTA) LLMs on this dataset. The results highlight the current state of fairness in LLMs and demonstrate the value of this benchmark for evaluating fairness of LLMs in more realistic multi-turn dialogue contexts. This underscores the need for future works to enhance LLM fairness and incorporate FairMT-1K in such efforts. Our code and dataset are available at https://github.com/FanZT6/FairMT-bench. | Fairness, Benchmark, Large language model | null | 7,273 | null |
OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text | https://openreview.net/forum?id=kwqhn2VuG4 | [
"Qingyun Li",
"Zhe Chen",
"Weiyun Wang",
"Wenhai Wang",
"Shenglong Ye",
"Zhenjiang Jin",
"Guanzhou Chen",
"Yinan He",
"Zhangwei Gao",
"Erfei Cui",
"Jiashuo Yu",
"Hao Tian",
"Jiasheng Zhou",
"Chao Xu",
"Bin Wang",
"Xingjian Wei",
"Wei Li",
"Wenjian Zhang",
"Bo Zhang",
"Pinlong Cai",
"et al. (18 additional authors not shown)"
] | Spotlight | Image-text interleaved data, consisting of multiple images and texts arranged in a natural document format, aligns with the presentation paradigm of internet data and closely resembles human reading habits. Recent studies have shown that such data aids multimodal in-context learning and maintains the capabilities of large language models during multimodal fine-tuning. However, the limited scale and diversity of current image-text interleaved data restrict the development of multimodal large language models. In this paper, we introduce OmniCorpus, a 10 billion-scale image-text interleaved dataset. Using an efficient data engine, we filter and extract large-scale high-quality documents, which contain 8.6 billion images and 1,696 billion text tokens. Compared to counterparts (e.g., MMC4, OBELICS), our dataset 1) has 15 times larger scales while maintaining good data quality; 2) features more diverse sources, including both English and non-English websites as well as video-centric websites; 3) is more flexible, easily degradable from an image-text interleaved format to pure text corpus and image-text pairs. Through comprehensive analysis and experiments, we validate the quality, usability, and effectiveness of the proposed dataset. We hope this could provide a solid data foundation for future multimodal model research. | Image-text interleaved dataset | We introduce OmniCorpus, the first 10 billion-level image-text interleaved dataset with diverse sources, providing a robust foundation for future multimodal model research. | 7,129 | 2406.08418 |
MAGNet: Motif-Agnostic Generation of Molecules from Scaffolds | https://openreview.net/forum?id=5FXKgOxmb2 | [
"Leon Hetzel",
"Johanna Sommer",
"Bastian Rieck",
"Fabian J Theis",
"Stephan Günnemann"
] | Spotlight | Recent advances in machine learning for molecules exhibit great potential for facilitating drug discovery from in silico predictions.
Most models for molecule generation rely on the decomposition of molecules into frequently occurring substructures (motifs), from which they generate novel compounds.
While motif representations greatly aid in learning molecular distributions, such methods fail to represent substructures beyond their known motif set, posing a fundamental limitation for discovering novel compounds.
To address this limitation and enhance structural expressivity, we propose to separate structure from features by abstracting motifs to scaffolds and, subsequently, allocating atom and bond types.
To this end, we introduce a novel factorisation of the molecules' data distribution that considers the entire molecular context and facilitates learning adequate assignments of atoms and bonds to scaffolds. Complementary to this, we propose MAGNet, the first model to freely learn motifs. Importantly, we demonstrate that MAGNet's improved expressivity leads to molecules with more structural diversity and, at the same time, diverse atom and bond assignments. | graph generative models, 2d molecules | null | 7,094 | null |
Broaden your SCOPE! Efficient Multi-turn Conversation Planning for LLMs with Semantic Space | https://openreview.net/forum?id=3cgMU3TyyE | [
"Zhiliang Chen",
"Xinyuan Niu",
"Chuan-Sheng Foo",
"Bryan Kian Hsiang Low"
] | Spotlight | Large language models (LLMs) are used in chatbots or AI assistants to hold conversations with a human user. In such applications, the quality (e.g., user engagement, safety) of a conversation is important and can only be exactly known at the end of the conversation. To maximize its expected quality, conversation planning reasons about the stochastic transitions within a conversation to select the optimal LLM response at each turn. Existing simulation-based conversation planning algorithms typically select the optimal response by simulating future conversations with a large number of LLM queries at every turn. However, this process is extremely time-consuming and hence impractical for real-time conversations. This paper presents a novel approach called Semantic space COnversation Planning with improved Efficiency (SCOPE) that exploits the dense semantic representation of conversations to perform conversation planning efficiently. In particular, SCOPE models the stochastic transitions in conversation semantics and their associated rewards to plan entirely within the semantic space. This allows us to select the optimal LLM response at every conversation turn without needing additional LLM queries for simulation. As a result, SCOPE can perform conversation planning 70 times faster than conventional simulation-based planning algorithms when applied to a wide variety of conversation starters and two reward functions seen in the real world, yet achieving a higher reward within a practical planning budget. Our code can be found at: https://github.com/chenzhiliang94/convo-plan-SCOPE. | Multi-turn Conversation Planning, Multi-turn LLM Optimization, MCTS, Semantic Space | Conversation planning typically uses many LLM queries for look-ahead simulation to select responses that maximize long-term rewards. By learning transition and reward models in text semantic space, we conversation plan without needing LLM queries. | 7,084 | null |
SPA-BENCH: A COMPREHENSIVE BENCHMARK FOR SMARTPHONE AGENT EVALUATION | https://openreview.net/forum?id=OZbFRNhpwr | [
"Jingxuan Chen",
"Derek Yuen",
"Bin Xie",
"Yuhao Yang",
"Gongwei Chen",
"Zhihao Wu",
"Li Yixing",
"Xurui Zhou",
"Weiwen Liu",
"Shuai Wang",
"Kaiwen Zhou",
"Rui Shao",
"Liqiang Nie",
"Yasheng Wang",
"Jianye HAO",
"Jun Wang",
"Kun Shao"
] | Spotlight | Smartphone agents are increasingly important for helping users control devices efficiently, with (Multimodal) Large Language Model (MLLM)-based approaches emerging as key contenders. Fairly comparing these agents is essential but challenging, requiring a varied task scope, the integration of agents with different implementations, and a generalisable evaluation pipeline to assess their strengths and weaknesses. In this paper, we present SPA-Bench, a comprehensive SmartPhone Agent Benchmark designed to evaluate (M)LLM-based agents in an interactive environment that simulates real-world conditions. SPA-Bench offers three key contributions: (1) A diverse set of tasks covering system and third-party apps in both English and Chinese, focusing on features commonly used in daily routines; (2) A plug-and-play framework enabling real-time agent interaction with Android devices, integrating over ten agents with the flexibility to add more; (3) A novel evaluation pipeline that automatically assesses agent performance across multiple dimensions, encompassing seven metrics related to task completion and resource consumption. Our extensive experiments across tasks and agents reveal challenges like interpreting mobile user interfaces, action grounding, memory retention, and execution costs. We propose future research directions to ease these difficulties, moving closer to real-world smartphone agent applications. | AI Agent, LLM, MLLM, Benchmark, Smartphone Control | A comprehensive benchmark for smartphone agents, evaluating multiple agents across smartphone control tasks in various scenarios. | 6,945 | null |
DeepRTL: Bridging Verilog Understanding and Generation with a Unified Representation Model | https://openreview.net/forum?id=2hcfoCHKoB | [
"Yi Liu",
"Changran XU",
"Yunhao Zhou",
"Zeju Li",
"Qiang Xu"
] | Spotlight | Recent advancements in large language models (LLMs) have shown significant potential for automating hardware description language (HDL) code generation from high-level natural language instructions. While fine-tuning has improved LLMs' performance in hardware design tasks, prior efforts have largely focused on Verilog generation, overlooking the equally critical task of Verilog understanding. Furthermore, existing models suffer from weak alignment between natural language descriptions and Verilog code, hindering the generation of high-quality, synthesizable designs. To address these issues, we present DeepRTL, a unified representation model that excels in both Verilog understanding and generation. Based on CodeT5+, DeepRTL is fine-tuned on a comprehensive dataset that aligns Verilog code with rich, multi-level natural language descriptions.
We also introduce the first benchmark for Verilog understanding and take the initiative to apply embedding similarity and GPT Score to evaluate the models' understanding capabilities. These metrics capture semantic similarity more accurately than traditional methods like BLEU and ROUGE, which are limited to surface-level n-gram overlaps. By adapting curriculum learning to train DeepRTL, we enable it to significantly outperform GPT-4 in Verilog understanding tasks, while achieving performance on par with OpenAI's o1-preview model in Verilog generation tasks. | Large Language Model, Program Representation Learning, Verilog Understanding and Generation | null | 6,938 | 2502.15832 |
Robust Function-Calling for On-Device Language Model via Function Masking | https://openreview.net/forum?id=yVQcr4qjD6 | [
"Qiqiang Lin",
"Muning Wen",
"Qiuying Peng",
"Guanyu Nie",
"Junwei Liao",
"Jun Wang",
"Xiaoyun Mo",
"Jiamu Zhou",
"Cheng Cheng",
"Yin Zhao",
"Jun Wang",
"Weinan Zhang"
] | Spotlight | Large language models have demonstrated impressive value in performing as autonomous agents when equipped with external tools and API calls. Nonetheless, effectively harnessing their potential for executing complex tasks crucially relies on enhancements in their function-calling capabilities. This paper identifies a critical gap in existing function-calling models, where performance varies significantly across benchmarks, often due to over-fitting to specific naming conventions. To address such an issue, we introduce Hammer, a novel family of foundation models specifically engineered for on-device function calling. Hammer employs an augmented dataset that enhances models’ sensitivity to irrelevant functions and incorporates function masking techniques to minimize over-fitting. Our empirical evaluations reveal that Hammer not only outperforms larger models but also demonstrates robust generalization across diverse benchmarks, achieving state-of-the-art results. Our open-source contributions include a specialized dataset for irrelevance detection, a tuning framework for enhanced generalization, and the Hammer models, establishing a new standard for function-calling performance. | language models, function-calling, mobile assistant, tool-using | null | 6,867 | null |
IGL-Bench: Establishing the Comprehensive Benchmark for Imbalanced Graph Learning | https://openreview.net/forum?id=uTqnyF0JNR | [
"Jiawen Qin",
"Haonan Yuan",
"Qingyun Sun",
"Lyujin Xu",
"Jiaqi Yuan",
"Pengfeng Huang",
"Zhaonan Wang",
"Xingcheng Fu",
"Hao Peng",
"Jianxin Li",
"Philip S. Yu"
] | Spotlight | Deep graph learning has gained grand popularity over the past years due to its versatility and success in representing graph data across a wide range of domains. However, the pervasive issue of imbalanced graph data distributions, where certain parts exhibit disproportionally abundant data while others remain sparse, undermines the efficacy of conventional graph learning algorithms, leading to biased outcomes. To address this challenge, Imbalanced Graph Learning (IGL) has garnered substantial attention, enabling more balanced data distributions and better task performance. Despite the proliferation of IGL algorithms, the absence of consistent experimental protocols and fair performance comparisons pose a significant barrier to comprehending advancements in this field. To bridge this gap, we introduce **IGL-Bench**, a foundational comprehensive benchmark for imbalanced graph learning, embarking on **17** diverse graph datasets and **24** distinct IGL algorithms with uniform data processing and splitting strategies. Specifically, IGL-Bench systematically investigates state-of-the-art IGL algorithms in terms of **effectiveness**, **robustness**, and **efficiency** on node-level and graph-level tasks, with the scope of class-imbalance and topology-imbalance. Extensive experiments demonstrate the potential benefits of IGL algorithms on various imbalanced conditions, offering insights and opportunities in the IGL field. Further, we have developed an open-sourced and unified package to facilitate reproducible evaluation and inspire further innovative research, available at: https://github.com/RingBDStack/IGL-Bench. | imbalanced graph learning, graph class-imbalance, graph topology-imbalance, comprehensive benchmark | We establish the IGL-Bench, a foundational comprehensive benchmark for imbalanced graph learning, embarking on 17 diverse graph datasets and 24 distinct IGL algorithms with uniform data processing and splitting strategies. | 6,858 | null |
Learning Equivariant Non-Local Electron Density Functionals | https://openreview.net/forum?id=FhBT596F1X | [
"Nicholas Gao",
"Eike Eberhard",
"Stephan Günnemann"
] | Spotlight | The accuracy of density functional theory hinges on the approximation of non-local contributions to the exchange-correlation (XC) functional. To date, machine-learned and human-designed approximations suffer from insufficient accuracy, limited scalability, or dependence on costly reference data. To address these issues, we introduce Equivariant Graph Exchange Correlation (EG-XC), a novel non-local XC functional based on equivariant graph neural networks (GNNs). Where previous works relied on semi-local functionals or fixed-size descriptors of the density, we compress the electron density into an SO(3)-equivariant nuclei-centered point cloud for efficient non-local atomic-range interactions. By applying an equivariant GNN on this point cloud, we capture molecular-range interactions in a scalable and accurate manner. To train EG-XC, we differentiate through a self-consistent field solver requiring only energy targets. In our empirical evaluation, we find EG-XC to accurately reconstruct `gold-standard' CCSD(T) energies on MD17. On out-of-distribution conformations of 3BPA, EG-XC reduces the relative MAE by 35% to 50%. Remarkably, EG-XC excels in data efficiency and molecular size extrapolation on QM9, matching force fields trained on 5 times more and larger molecules. On identical training sets, EG-XC yields on average 51% lower MAEs. | Density Functional Theory, DFT, Functional, Exchange Correlation, XC, Equivariance, Graph Neural Network, Electron Density, Kohn-Sham DFT | We propose an equivariant graph neural network-driven exchange correlation functional for Kohn-Sham DFT. | 6,847 | 2410.07972 |
PaRa: Personalizing Text-to-Image Diffusion via Parameter Rank Reduction | https://openreview.net/forum?id=KZgo2YQbhc | [
"Shangyu Chen",
"Zizheng Pan",
"Jianfei Cai",
"Dinh Phung"
] | Spotlight | Personalizing a large-scale pretrained Text-to-Image (T2I) diffusion model is chal-
lenging as it typically struggles to make an appropriate trade-off between its training
data distribution and the target distribution, i.e., learning a novel concept with only a
few target images to achieve personalization (aligning with the personalized target)
while preserving text editability (aligning with diverse text prompts). In this paper,
we propose PaRa, an effective and efficient Parameter Rank Reduction approach
for T2I model personalization by explicitly controlling the rank of the diffusion
model parameters to restrict its initial diverse generation space into a small and
well-balanced target space. Our design is motivated by the fact that taming a T2I
model toward a novel concept such as a specific art style implies a small generation
space. To this end, by reducing the rank of model parameters during finetuning, we
can effectively constrain the space of the denoising sampling trajectories towards
the target. With comprehensive experiments, we show that PaRa achieves great
advantages over existing finetuning approaches on single/multi-subject generation
as well as single-image editing. Notably, compared to the prevailing fine-tuning
technique LoRA, PaRa achieves better parameter efficiency (2× fewer learnable
parameters) and much better target image alignment. | Text-to-Image diffusion model, Diffusion model fine-tuning | Achieve lightweight and reliable personalized models through the subspace of the pre-trained Stable Diffusion model. | 6,805 | 2406.05641 |
Near-Optimal Online Learning for Multi-Agent Submodular Coordination: Tight Approximation and Communication Efficiency | https://openreview.net/forum?id=i8dYPGdB1C | [
"Qixin Zhang",
"Zongqi Wan",
"Yu Yang",
"Li Shen",
"Dacheng Tao"
] | Spotlight | Coordinating multiple agents to collaboratively maximize submodular functions in unpredictable environments is a critical task with numerous applications in machine learning, robot planning and control. The existing approaches, such as the OSG algorithm, are often hindered by their poor approximation guarantees and the rigid requirement for a fully connected communication graph. To address these challenges, we firstly present a $\textbf{MA-OSMA}$ algorithm, which employs the multi-linear extension to transfer the discrete submodular maximization problem into a continuous optimization, thereby allowing us to reduce the strict dependence on a complete graph through consensus techniques. Moreover, $\textbf{MA-OSMA}$ leverages a novel surrogate gradient to avoid sub-optimal stationary points. To eliminate the computationally intensive projection operations in $\textbf{MA-OSMA}$, we also introduce a projection-free $\textbf{MA-OSEA}$ algorithm, which effectively utilizes the KL divergence by mixing a uniform distribution. Theoretically, we confirm that both algorithms achieve a regret bound of $\widetilde{O}(\sqrt{\frac{C_{T}T}{1-\beta}})$ against a $(\frac{1-e^{-c}}{c})$-approximation to the best comparator in hindsight, where $C_{T}$ is the deviation of maximizer sequence, $\beta$ is the spectral gap of the network and $c$ is the joint curvature of submodular objectives. This result significantly improves the $(\frac{1}{1+c})$-approximation provided by the state-of-the-art OSG algorithm. Finally, we demonstrate the effectiveness of our proposed algorithms through simulation-based multi-target tracking. | Online Learning, Submodular Maximization, Surrogate Gradient, Multi-Agent | null | 6,794 | 2502.05028 |
PETRA: Parallel End-to-end Training with Reversible Architectures | https://openreview.net/forum?id=0fhzSFsGUT | [
"Stephane Rivaud",
"Louis Fournier",
"Thomas Pumir",
"Eugene Belilovsky",
"Michael Eickenberg",
"Edouard Oyallon"
] | Spotlight | Reversible architectures have been shown to be capable of performing on par with their non-reversible architectures, being applied in deep learning for memory savings and generative modeling. In this work, we show how reversible architectures can solve challenges in parallelizing deep model training. We introduce PETRA, a novel alternative to backpropagation for parallelizing gradient computations. PETRA facilitates effective model parallelism by enabling stages (i.e., a set of layers) to compute independently on different devices, while only needing to communicate activations and gradients between each other. By decoupling the forward and backward passes and keeping a single updated version of the parameters, the need for weight stashing is also removed. We develop a custom autograd-like training framework for PETRA, and we demonstrate its effectiveness on standard computer vision benchmarks, achieving competitive accuracies comparable to backpropagation using ResNet-18, ResNet-34, and ResNet-50 models. | Model parallelism, Delayed gradient, Reversible architectures | We show how combining reversible architectures with delayed gradient approaches for model parallelism allows to achieve computational speedups with drastic memory reduction. | 6,763 | 2406.02052 |
BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics | https://openreview.net/forum?id=dRXxFEY8ZE | [
"Lukas Rauch",
"Raphael Schwinger",
"Moritz Wirth",
"René Heinrich",
"Denis Huseljic",
"Marek Herde",
"Jonas Lange",
"Stefan Kahl",
"Bernhard Sick",
"Sven Tomforde",
"Christoph Scholz"
] | Spotlight | Deep learning (DL) has greatly advanced audio classification, yet the field is limited by the scarcity of large-scale benchmark datasets that have propelled progress in other domains. While AudioSet is a pivotal step to bridge this gap as a universal-domain dataset, its restricted accessibility and limited range of evaluation use cases challenge its role as the sole resource. Therefore, we introduce BirdSet, a large-scale benchmark data set for audio classification focusing on avian bioacoustics. BirdSet surpasses AudioSet with over 6,800 recording hours ($\uparrow17\%$) from nearly 10,000 classes ($\uparrow18\times$) for training and more than 400 hours ($\uparrow7\times$) across eight strongly labeled evaluation datasets. It serves as a versatile resource for use cases such as multi-label classification, covariate shift or self-supervised learning. We benchmark six well-known DL models in multi-label classification across three distinct training scenarios and outline further evaluation use cases in audio classification. We host our dataset on Hugging Face for easy accessibility and offer an extensive codebase to reproduce our results. | audio classification, multi-label, dataset collection, bioacoustics | We introduce BirdSet, a multipurpose and large-scale dataset collection for audio classification. | 6,728 | 2403.10380 |
SePer: Measure Retrieval Utility Through The Lens Of Semantic Perplexity Reduction | https://openreview.net/forum?id=ixMBnOhFGd | [
"Lu Dai",
"Yijie Xu",
"Jinhui Ye",
"Hao Liu",
"Hui Xiong"
] | Spotlight | Large Language Models (LLMs) have demonstrated improved generation performance by incorporating externally retrieved knowledge, a process known as retrieval-augmented generation (RAG). Despite the potential of this approach, existing studies evaluate RAG effectiveness by 1) assessing retrieval and generation components jointly, which obscures retrieval's distinct contribution, or 2) examining retrievers using traditional metrics such as NDCG, which creates a gap in understanding retrieval's true utility in the overall generation process. To address the above limitations, in this work, we introduce an automatic evaluation method that measures retrieval quality through the lens of information gain within the RAG framework. Specifically, we propose Semantic Perplexity (SePer), a metric that captures the LLM's internal belief about the correctness of the retrieved information. We quantify the utility of retrieval by the extent to which it reduces semantic perplexity post-retrieval. Extensive experiments demonstrate that SePer not only aligns closely with human preferences but also offers a more precise and efficient evaluation of retrieval utility across diverse RAG scenarios. | information retrieval, metric | null | 6,677 | 2503.01478 |
Physics-aligned field reconstruction with diffusion bridge | https://openreview.net/forum?id=D042vFwJAM | [
"Zeyu Li",
"Hongkun Dou",
"Shen Fang",
"Wang Han",
"Yue Deng",
"Lijun Yang"
] | Spotlight | The reconstruction of physical fields from sparse measurements is pivotal in both scientific research and engineering applications. Traditional methods are increasingly supplemented by deep learning models due to their efficacy in extracting features from data. However, except for the low accuracy on complex physical systems, these models often fail to comply with essential physical constraints, such as governing equations and boundary conditions. To overcome this limitation, we introduce a novel data-driven field reconstruction framework, termed the Physics-aligned Schr\"{o}dinger Bridge (PalSB). This framework leverages a diffusion bridge mechanism that is specifically tailored to align with physical constraints. The PalSB approach incorporates a dual-stage training process designed to address both local reconstruction mapping and global physical principles. Additionally, a boundary-aware sampling technique is implemented to ensure adherence to physical boundary conditions. We demonstrate the effectiveness of PalSB through its application to three complex nonlinear systems: cylinder flow from Particle Image Velocimetry experiments, two-dimensional turbulence, and a reaction-diffusion system. The results reveal that PalSB not only achieves higher accuracy but also exhibits enhanced compliance with physical constraints compared to existing methods. This highlights PalSB's capability to generate high-quality representations of intricate physical interactions, showcasing its potential for advancing field reconstruction techniques. The source code can be found at https://github.com/lzy12301/PalSB. | Fluid dynamics, diffusion models, super-resolution | We propose a diffusion-based framework for the reconstruction of physical field with enhanced compliance with physical laws. | 6,648 | null |
RegMix: Data Mixture as Regression for Language Model Pre-training | https://openreview.net/forum?id=5BjQOUXq7i | [
"Qian Liu",
"Xiaosen Zheng",
"Niklas Muennighoff",
"Guangtao Zeng",
"Longxu Dou",
"Tianyu Pang",
"Jing Jiang",
"Min Lin"
] | Spotlight | The data mixture for large language model pre-training significantly impacts performance, yet how to determine an effective mixture remains unclear. We propose RegMix to automatically identify a high-performing data mixture by formulating it as a regression task. RegMix trains many small models on diverse data mixtures, uses regression to predict performance of unseen mixtures, and applies the best predicted mixture to train a large-scale model with orders of magnitude more compute. To empirically validate RegMix, we train 512 models with 1M parameters for 1B tokens to fit the regression model and predict the best data mixture. Using this mixture we train a 1B parameter model for 25B tokens (i.e. 1000× larger and 25× longer) which we find performs best among 64 candidate 1B parameter models with other mixtures. Furthermore, RegMix consistently outperforms human selection in experiments involving models up to 7B models trained on 100B tokens, while matching or exceeding DoReMi using just 10% of the computational resources. Our experiments also show that (1) Data mixtures significantly impact performance; (2) Web corpora rather than data perceived as high-quality like Wikipedia have the strongest positive correlation with downstream performance; (3) Domains interact in complex ways often contradicting common sense, thus automatic approaches like RegMix are needed; (4) Data mixture effects transcend scaling laws. Our code is available at https://github.com/sail-sg/regmix. | language model pre-training, data mixture, regression | We introduce RegMix, an automated data mixture method that formulates data mixture as a regression problem. RegMix achieves a 6.3% improvement over human selection on the HellaSwag benchmark, with only a 2% extra training FLOPs. | 6,642 | 2407.01492 |
When Attention Sink Emerges in Language Models: An Empirical View | https://openreview.net/forum?id=78Nn4QJTEN | [
"Xiangming Gu",
"Tianyu Pang",
"Chao Du",
"Qian Liu",
"Fengzhuo Zhang",
"Cunxiao Du",
"Ye Wang",
"Min Lin"
] | Spotlight | Auto-regressive language Models (LMs) assign significant attention to the first token, even if it is not semantically important, which is known as **attention sink**. This phenomenon has been widely adopted in applications such as streaming/long context generation, KV cache optimization, inference acceleration, model quantization, and others. Despite its widespread use, a deep understanding of attention sink in LMs is still lacking. In this work, we first demonstrate that attention sinks exist universally in auto-regressive LMs with various inputs, even in small models. Furthermore, attention sink is observed to emerge during the LM pre-training, motivating us to investigate how *optimization*, *data distribution*, *loss function*, and *model architecture* in LM pre-training influence its emergence. We highlight that attention sink emerges after effective optimization on sufficient training data. The sink position is highly correlated with the loss function and data distribution. Most importantly, we find that attention sink acts more like key biases, *storing extra attention scores*, which could be non-informative and not contribute to the value computation. We also observe that this phenomenon (at least partially) stems from tokens' inner dependence on attention scores as a result of softmax normalization. After relaxing such dependence by replacing softmax attention with other attention operations, such as sigmoid attention without normalization, attention sinks do not emerge in LMs up to 1B parameters. The code is available at https://github.com/sail-sg/Attention-Sink. | Attention Sink, Language Models, Empirical Study | We conduct extensive experiments to empirically understand when attention sink emerges in language models. | 6,630 | 2410.10781 |
PianoMotion10M: Dataset and Benchmark for Hand Motion Generation in Piano Performance | https://openreview.net/forum?id=rxVvRBgqmS | [
"Qijun Gan",
"Song Wang",
"Shengtao Wu",
"Jianke Zhu"
] | Spotlight | Recently, artificial intelligence techniques for education have been received increasing attentions, while it still remains an open problem to design the effective music instrument instructing systems. Although key presses can be directly derived from sheet music, the transitional movements among key presses require more extensive guidance in piano performance. In this work, we construct a piano-hand motion generation benchmark to guide hand movements and fingerings for piano playing. To this end, we collect an annotated dataset, PianoMotion10M, consisting of 116 hours of piano playing videos from a bird's-eye view with 10 million annotated hand poses. We also introduce a powerful baseline model that generates hand motions from piano audios through a position predictor and a position-guided gesture generator. Furthermore, a series of evaluation metrics are designed to assess the performance of the baseline model, including motion similarity, smoothness, positional accuracy of left and right hands, and overall fidelity of movement distribution. Despite that piano key presses with respect to music scores or audios are already accessible, PianoMotion10M aims to provide guidance on piano fingering for instruction purposes. The source code and dataset can be accessed at https://github.com/agnJason/PianoMotion10M. | Hand pose estimation, piano music, motion generation | We construct the first large-scale piano-motion dataset, PianoMotion10M for hand motion generation. | 6,629 | 2406.09326 |
The Power of LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions | https://openreview.net/forum?id=ws5phQki00 | [
"Stefan Sylvius Wagner",
"Maike Behrendt",
"Marc Ziegele",
"Stefan Harmeling"
] | Spotlight | Stance detection holds great potential to improve online political discussions through its deployment in discussion platforms for purposes such as content moderation, topic summarisation or to facilitate more balanced discussions. Typically, transformer-based models are employed directly for stance detection, requiring vast amounts of data. However, the wide variety of debate topics in online political discussions makes data collection particularly challenging. LLMs have revived stance detection, but their online deployment in online political discussions faces challenges like inconsistent outputs, biases, and vulnerability to adversarial attacks. We show how LLM-generated synthetic data can improve stance detection for online political discussions by using reliable traditional stance detection models for online deployment, while leveraging the text generation capabilities of LLMs for synthetic data generation in a secure offline environment. To achieve this, (i) we generate synthetic data for specific debate questions by prompting a Mistral-7B model and show that fine-tuning with the generated synthetic data can substantially improve the performance of stance detection, while remaining interpretable and aligned with real world data. (ii) Using the synthetic data as a reference, we can improve performance even further by identifying the most informative samples in an unlabelled dataset, i.e., those samples which the stance detection model is most uncertain about and can benefit from the most. By fine-tuning with both synthetic data and the most informative samples, we surpass the performance of the baseline model that is fine-tuned on all true labels, while labelling considerably less data. | large language models, stance detection, data augmentation, active learning, online political discussions | We study and show how to leverage LLM-generated synthetic data for stance detection in online discussions, which is a challenging stance detection task because of the broad range of debate questions. | 6,567 | 2406.12480 |
Tell me about yourself: LLMs are aware of their learned behaviors | https://openreview.net/forum?id=IjQ2Jtemzy | [
"Jan Betley",
"Xuchan Bao",
"Martín Soto",
"Anna Sztyber-Betley",
"James Chua",
"Owain Evans"
] | Spotlight | We study *behavioral self-awareness*, which we define as an LLM's capability to articulate its behavioral policies without relying on in-context examples. We finetune LLMs on examples that exhibit particular behaviors, including (a) making risk-seeking / risk-averse economic decisions, and (b) making the user say a certain word. Although these examples never contain explicit descriptions of the policy (e.g. "I will now take the risk-seeking option"), we find that the finetuned LLMs can explicitly describe their policies through out-of-context reasoning. We demonstrate LLMs' behavioral self-awareness across various evaluation tasks, both for multiple-choice and free-form questions.
Furthermore, we demonstrate that models can correctly attribute different learned policies to distinct personas.
Finally, we explore the connection between behavioral self-awareness and the concept of backdoors in AI safety, where certain behaviors are implanted in a model, often through data poisoning, and can be triggered under certain conditions. We find evidence that LLMs can recognize the existence of the backdoor-like behavior that they have acquired through fine-tuning. | NLP, LLM, GPT, generalization, out-of-context reasoning, capabilities, fine-tuning, self-awareness, self-knowledge | LLMs finetuned to follow an implicit policy can later explicitly describe that policy. | 6,554 | 2501.11120 |
COPER: Correlation-based Permutations for Multi-View Clustering | https://openreview.net/forum?id=5ZEbpBYGwH | [
"Ran Eisenberg",
"Jonathan Svirsky",
"Ofir Lindenbaum"
] | Spotlight | Combining data from different sources can improve data analysis tasks such as clustering. However, most of the current multi-view clustering methods are limited to specific domains or rely on a suboptimal and computationally intensive two-stage process of representation learning and clustering. We propose an end-to-end deep learning-based multi-view clustering framework for general data types (such as images and tables). Our approach involves generating meaningful fused representations using a novel permutation-based canonical correlation objective. We provide a theoretical analysis showing how the learned embeddings approximate those obtained by supervised linear discriminant analysis (LDA). Cluster assignments are learned by identifying consistent pseudo-labels across multiple views. Additionally, we establish a theoretical bound on the error caused by incorrect pseudo-labels in the unsupervised representations compared to LDA. Extensive experiments on ten multi-view clustering benchmark datasets provide empirical evidence for the effectiveness of the proposed model. | clustering, canonical correlation analysis, self supervision, multiview | null | 6,526 | null |
Improving Convergence Guarantees of Random Subspace Second-order Algorithm for Nonconvex Optimization | https://openreview.net/forum?id=tuu4de7HL1 | [
"Rei Higuchi",
"Pierre-Louis Poirion",
"Akiko Takeda"
] | Spotlight | In recent years, random subspace methods have been actively studied for large-dimensional nonconvex problems. Recent subspace methods have improved theoretical guarantees such as iteration complexity and local convergence rate while reducing computational costs by deriving descent directions in randomly selected low-dimensional subspaces. This paper proposes the Random Subspace Homogenized Trust Region (RSHTR) method with the best theoretical guarantees among random subspace algorithms for nonconvex optimization. RSHTR achieves an $\varepsilon$-approximate first-order stationary point in $O(\varepsilon^{-3/2})$ iterations, converging locally at a linear rate. Furthermore, under rank-deficient conditions, RSHTR satisfies $\varepsilon$-approximate second-order necessary conditions in $O(\varepsilon^{-3/2})$ iterations and exhibits a local quadratic convergence. Experiments on real-world datasets verify the benefits of RSHTR. | random projection, trust region method, non-convex optimization, second-order stationary point, local convergence | This paper proposes the Random Subspace Homogenized Trust Region (RSHTR) method with the best theoretical guarantees among random subspace algorithms for non-convex optimization. | 6,508 | 2406.14337 |
Revisiting text-to-image evaluation with Gecko: on metrics, prompts, and human rating | https://openreview.net/forum?id=Im2neAMlre | [
"Olivia Wiles",
"Chuhan Zhang",
"Isabela Albuquerque",
"Ivana Kajic",
"Su Wang",
"Emanuele Bugliarello",
"Yasumasa Onoe",
"Pinelopi Papalampidi",
"Ira Ktena",
"Christopher Knutsen",
"Cyrus Rashtchian",
"Anant Nawalgaria",
"Jordi Pont-Tuset",
"Aida Nematzadeh"
] | Spotlight | While text-to-image (T2I) generative models have become ubiquitous, they do not necessarily generate images that align with a given prompt.
While many metrics and benchmarks have been proposed to evaluate T2I models and alignment metrics, the impact of the evaluation components (prompt sets, human annotations, evaluation task) has not been systematically measured.
We find that looking at only *one slice of data*, i.e. one set of capabilities or human annotations, is not enough to obtain stable conclusions that generalise to new conditions or slices when evaluating T2I models or alignment metrics.
We address this by introducing an evaluation suite of $>$100K annotations across four human annotation templates that comprehensively evaluates models' capabilities across a range of methods for gathering human annotations and comparing models.
In particular, we propose (1) a carefully curated set of prompts -- *Gecko2K*; (2) a statistically grounded method of comparing T2I models; and (3) how to systematically evaluate metrics under three *evaluation tasks* -- *model ordering, pair-wise instance scoring, point-wise instance scoring*.
Using this evaluation suite, we evaluate a wide range of metrics and find that a metric may do better in one setting but worse in another.
As a result, we introduce a new, interpretable auto-eval metric that is consistently better correlated with human ratings than such existing metrics on our evaluation suite--across different human templates and evaluation settings--and on TIFA160. | text-to-image evaluation; text-to-image alignment; human evaluation; | We create a large benchmark for T2I alignment to evaluate models and metrics across skills, evaluation tasks, and human annotation templates. | 6,464 | null |
Diffusion Bridge AutoEncoders for Unsupervised Representation Learning | https://openreview.net/forum?id=hBGavkf61a | [
"Yeongmin Kim",
"Kwanghyeon Lee",
"Minsang Park",
"Byeonghu Na",
"Il-chul Moon"
] | Spotlight | Diffusion-based representation learning has achieved substantial attention due to its promising capabilities in latent representation and sample generation. Recent studies have employed an auxiliary encoder to identify a corresponding representation from data and to adjust the dimensionality of a latent variable $\mathbf{z}$. Meanwhile, this auxiliary structure invokes an *information split problem*; the information of each data instance $\mathbf{x}_0$ is divided into diffusion endpoint $\mathbf{x}_T$ and encoded $\mathbf{z}$ because there exist two inference paths starting from the data. The latent variable modeled by diffusion endpoint $\mathbf{x}_T$ has some disadvantages. The diffusion endpoint $\mathbf{x}_T$ is computationally expensive to obtain and inflexible in dimensionality. To address this problem, we introduce Diffusion Bridge AuteEncoders (DBAE), which enables $\mathbf{z}$-dependent endpoint $\mathbf{x}_T$ inference through a feed-forward architecture. This structure creates an information bottleneck at $\mathbf{z}$, so $\mathbf{x}_T$ becomes dependent on $\mathbf{z}$ in its generation. This results in $\mathbf{z}$ holding the full information of data. We propose an objective function for DBAE to enable both reconstruction and generative modeling, with their theoretical justification. Empirical evidence supports the effectiveness of the intended design in DBAE, which notably enhances downstream inference quality, reconstruction, and disentanglement. Additionally, DBAE generates high-fidelity samples in the unconditional generation. Our code is
available at https://github.com/aailab-kaist/DBAE. | Diffusion Model, Represenation Learning, Autoencoders | This paper introduces Diffusion Bridge Autoencoders (DBAE) to design encoder dependent endpoint inference. | 6,348 | 2405.17111 |
Bundle Neural Network for message diffusion on graphs | https://openreview.net/forum?id=scI9307PLG | [
"Jacob Bamberger",
"Federico Barbero",
"Xiaowen Dong",
"Michael M. Bronstein"
] | Spotlight | The dominant paradigm for learning on graphs is message passing. Despite being a strong inductive bias, the local message passing mechanism faces challenges such as over-smoothing, over-squashing, and limited expressivity. To address these issues, we introduce Bundle Neural Networks (BuNNs), a novel graph neural network architecture that operates via *message diffusion* on *flat vector bundles* — geometrically inspired structures that assign to each node a vector space and an orthogonal map. A BuNN layer evolves node features through a diffusion-type partial differential equation, where its discrete form acts as a special case of the recently introduced Sheaf Neural Network (SNN), effectively alleviating over-smoothing. The continuous nature of message diffusion enables BuNNs to operate at larger scales, reducing over-squashing. We establish the universality of BuNNs in approximating feature transformations on infinite families of graphs with injective positional encodings, marking the first positive expressivity result of its kind. We support our claims with formal analysis and synthetic experiments. Empirically, BuNNs perform strongly on heterophilic and long-range tasks, which demonstrates their robustness on a diverse range of challenging real-world tasks. | graph neural network, sheaf neural network, geometric deep learning, algebraic topology, vector bundles, expressivity | We propose Bundle Neural Networks, a new type of Graph Neural Network that operates via message diffusion, a continuous version of message-passing that allows to mitigate over-smoothing, over-squashing, and is provably expressive. | 6,260 | null |
SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints | https://openreview.net/forum?id=uvHmnahyp1 | [
"Miruna Cretu",
"Charles Harris",
"Ilia Igashov",
"Arne Schneuing",
"Marwin Segler",
"Bruno Correia",
"Julien Roy",
"Emmanuel Bengio",
"Pietro Lio"
] | Spotlight | Generative models see increasing use in computer-aided drug design. However, while performing well at capturing distributions of molecular motifs, they often produce synthetically inaccessible molecules. To address this, we introduce SynFlowNet, a GFlowNet model whose action space uses chemical reactions and buyable reactants to sequentially build new molecules. By incorporating forward synthesis as an explicit constraint of the generative mechanism, we aim at bridging the gap between in silico molecular generation and real world synthesis capabilities. We evaluate our approach using synthetic accessibility scores and an independent retrosynthesis tool to assess the synthesizability of our compounds, and motivate the choice of GFlowNets through considerable improvement in sample diversity compared to baselines. Additionally, we identify challenges with reaction encodings that can complicate traversal of the MDP in the backward direction. To address this, we introduce various strategies for learning the GFlowNet backward policy and thus demonstrate how additional constraints can be integrated into the GFlowNet MDP framework. This approach enables our model to successfully identify synthesis pathways for previously unseen molecules. | GFlowNets, de novo molecular generation, synthesizable molecular design | A GFlowNet with an action space of chemical reactions and a trained backward policy. | 6,178 | 2405.01155 |
u-μP: The Unit-Scaled Maximal Update Parametrization | https://openreview.net/forum?id=P7KRIiLM8T | [
"Charlie Blake",
"Constantin Eichenberg",
"Josef Dean",
"Lukas Balles",
"Luke Yuri Prince",
"Björn Deiseroth",
"Andres Felipe Cruz-Salinas",
"Carlo Luschi",
"Samuel Weinbach",
"Douglas Orr"
] | Spotlight | The Maximal Update Parametrization ($\mu$P) aims to make the optimal hyperparameters (HPs) of a model independent of its size, allowing them to be swept using a cheap proxy model rather than the full-size target model. We present a new scheme, u-$\mu$P, which improves upon $\mu$P by combining it with Unit Scaling, a method for designing models that makes them easy to train in low-precision. The two techniques have a natural affinity: $\mu$P ensures that the scale of activations is independent of model size, and Unit Scaling ensures that activations, weights and gradients begin training with a scale of one. This synthesis opens the door to a simpler scheme, whose default values are near-optimal. This in turn facilitates a more efficient sweeping strategy, with u-$\mu$P models reaching a lower loss than comparable $\mu$P models and working out-of-the-box in FP8. | maximal update parametrization, learning dynamics, hyperparameter transfer, efficiency, training, stability, scaling, numerics, fp8, low precision | We improve µP by combining it with Unit Scaling, leading to a simpler scheme with better default hyperparameters, lower loss, more efficient sweeping and simple FP8 training. | 6,104 | null |
Improved Convergence Rate for Diffusion Probabilistic Models | https://openreview.net/forum?id=SOd07Qxkw4 | [
"Gen Li",
"Yuchen Jiao"
] | Spotlight | Score-based diffusion models have achieved remarkable empirical performance in the field of machine learning and artificial intelligence for their ability to generate high-quality new data instances from complex distributions. Improving our understanding of diffusion models, including mainly convergence analysis for such models, has attracted a lot of interests. Despite a lot of theoretical attempts, there still exists significant gap between theory and practice. Towards to close this gap, we establish an iteration complexity at the order of $d^{1/3}\varepsilon^{-2/3}$, which is better than $d^{5/12}\varepsilon^{-1}$, the best known complexity achieved before our work. This convergence analysis is based on a randomized midpoint method, which is first proposed for log-concave sampling (Shen & Lee, 2019), and then extended to diffusion models by Gupta et al. (2024). Our theory accommodates $\varepsilon$-accurate score estimates, and does not require log-concavity on the target distribution. Moreover, the algorithm can also be parallelized to run in only $O(\log^2(d/\varepsilon))$ parallel rounds in a similar way to prior works. | score-based generative model, diffusion model, probability flow ODE, randomized learning rate | null | 6,073 | 2410.13738 |
MagicPIG: LSH Sampling for Efficient LLM Generation | https://openreview.net/forum?id=ALzTQUgW8a | [
"Zhuoming Chen",
"Ranajoy Sadhukhan",
"Zihao Ye",
"Yang Zhou",
"Jianyu Zhang",
"Niklas Nolte",
"Yuandong Tian",
"Matthijs Douze",
"Leon Bottou",
"Zhihao Jia",
"Beidi Chen"
] | Spotlight | Large language models (LLMs) with long context windows have gained significant attention. However, the KV cache, stored to avoid re-computation, becomes a bottleneck. Various dynamic sparse or TopK-based attention approximation methods have been proposed to leverage the common insight that attention is sparse. In this paper, we first show that TopK attention itself suffers from quality degradation in certain downstream tasks because attention is not always as sparse as expected. Rather than selecting the keys and values with the highest attention scores, sampling with theoretical guarantees can provide a better estimation for attention output. To make the sampling-based approximation practical in LLM generation, we propose MagicPIG, a heterogeneous system based on Locality Sensitive Hashing (LSH). MagicPIG significantly reduces the workload of attention computation while preserving high accuracy for diverse tasks. MagicPIG stores the LSH hash tables and runs the attention computation on the CPU, which allows it to serve longer contexts and larger batch sizes with high approximation accuracy. MagicPIG can improve decoding throughput by up to $5\times$ across various GPU hardware and achieve 54ms decoding latency on a single RTX 4090 for Llama-3.1-8B-Instruct model with a context of 96k tokens. | locality sensitive hashing, randomized algorithms, llm inference, kv cache | We leverage Locality Sensitive Hashing (LSH) sampling to accelerate LLM decoding. | 6,043 | 2410.16179 |
Streamlining Redundant Layers to Compress Large Language Models | https://openreview.net/forum?id=IC5RJvRoMp | [
"Xiaodong Chen",
"Yuxuan Hu",
"Jing Zhang",
"Yanling Wang",
"Cuiping Li",
"Hong Chen"
] | Spotlight | This paper introduces LLM-Streamline, a pioneer work on layer pruning for large language models (LLMs). It is based on the observation that different layers have varying impacts on hidden states, enabling the identification of less important layers to be pruned.
LLM-Streamline comprises two parts: layer pruning, which removes consecutive layers with the lowest importance based on target sparsity, and layer replacement, a novel module that trains a lightweight network to replace the pruned layers to mitigate performance loss. Additionally, a new metric called stability is proposed to address the limitations of the widely used accuracy metric in evaluating model compression. Experiments show that LLM-Streamline outperforms both previous and concurrent state-of-the-art pruning methods in terms of both performance and training efficiency. Our code is available at \href{https://github.com/RUCKBReasoning/LLM-Streamline}{this repository}. | large language models, model compression, structured pruning | null | 6,009 | 2403.19135 |
Rethinking and Improving Autoformalization: Towards a Faithful Metric and a Dependency Retrieval-based Approach | https://openreview.net/forum?id=hUb2At2DsQ | [
"Qi Liu",
"Xinhao Zheng",
"Xudong Lu",
"Qinxiang Cao",
"Junchi Yan"
] | Spotlight | As a central component in formal verification, statement autoformalization has been widely studied including the recent efforts from machine learning community, but still remains a widely-recognized difficult and open problem. In this paper, we delve into two critical yet under-explored gaps: 1) absence of faithful and universal automated evaluation for autoformalization results; 2) agnosia of contextual information, inducing severe hallucination of formal definitions and theorems. To address the first issue, we propose **BEq** (_**B**idirectional **E**xtended Definitional E**q**uivalence_), an automated neuro-symbolic method to determine the equivalence between two formal statements, which is formal-grounded and well-aligned with human intuition. For the second, we propose **RAutoformalizer** (_**R**etrieval-augmented **Autoformalizer**_), augmenting statement autoformalization by _Dependency Retrieval_, retrieving potentially dependent objects from formal libraries. We parse the dependencies of libraries and propose to _structurally informalise_ formal objects by the topological order of dependencies. To evaluate OOD generalization and research-level capabilities, we build a novel benchmark, _Con-NF_, consisting of 961 informal-formal statement pairs from frontier mathematical researches. Experiments validate the effectiveness of our approaches: BEq is evaluated on 200 diverse formal statement pairs with expert-annotated equivalence label, exhibiting significantly improved accuracy ($82.50\\% \mapsto 90.50\\%$) and precision ($70.59\\% \mapsto 100.0\\%$). For dependency retrieval, a strong baseline is devised. Our RAutoformalizer substantially outperforms SOTA baselines in both in-distribution ProofNet benchmark ($12.83\\% \mapsto 18.18\\%$, BEq@8) and OOD Con-NF scenario ($4.58\\%\mapsto 16.86\\%$, BEq@8). | Large Language Model, Formal Verification, Autoformalization | For statement autoformalization, we propose a faithful neural-symbolic evaluation method, a new task "dependency retrieval", a new data synthesis method, a research-level benchmark and a new paradigm that is augmented by dependency retrieval. | 5,989 | null |
Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning | https://openreview.net/forum?id=k03mB41vyM | [
"Patrik Reizinger",
"Siyuan Guo",
"Ferenc Huszár",
"Bernhard Schölkopf",
"Wieland Brendel"
] | Spotlight | Identifying latent representations or causal structures is important for good generalization and downstream task performance. However, both fields developed rather independently.
We observe that several structure and representation identifiability methods, particularly those that require multiple environments, rely on
exchangeable non--i.i.d. (independent and identically distributed) data.
To formalize this connection,
we propose the Identifiable Exchangeable Mechanisms (IEM) framework to unify key representation and causal structure learning methods. IEM provides a unified probabilistic graphical model encompassing causal discovery, Independent Component Analysis, and Causal Representation Learning.
With the help of the IEM model, we generalize the Causal de Finetti theorem of Guo et al., 2022 by relaxing the necessary conditions for causal structure identification in exchangeable data.
We term these conditions cause and mechanism variability, and show how they imply a duality condition in identifiable representation learning, leading to new identifiability results. | causality, ICA, identifiability, causal representation learning | A unfiying frameworkt for identifiable causal structure and representation learning method under the lens of exchangeability | 5,985 | 2406.14302 |
Learning Spatiotemporal Dynamical Systems from Point Process Observations | https://openreview.net/forum?id=37EXtKCOkn | [
"Valerii Iakovlev",
"Harri Lähdesmäki"
] | Spotlight | Spatiotemporal dynamics models are fundamental for various domains, from heat propagation in materials to oceanic and atmospheric flows. However, currently available neural network-based spatiotemporal modeling approaches fall short when faced with data that is collected randomly over time and space, as is often the case with sensor networks in real-world applications like crowdsourced earthquake detection or pollution monitoring. In response, we developed a new method that can effectively learn spatiotemporal dynamics from such point process observations. Our model integrates techniques from neural differential equations, neural point processes, implicit neural representations and amortized variational inference to model both the dynamics of the system and the probabilistic locations and timings of observations. It outperforms existing methods on challenging spatiotemporal datasets by offering substantial improvements in predictive accuracy and computational efficiency, making it a useful tool for modeling and understanding complex dynamical systems observed under realistic, unconstrained conditions. | dynamics, spatiotemporal, neural, PDE, ODE | null | 5,963 | 2406.00368 |
Probabilistic Neural Pruning via Sparsity Evolutionary Fokker-Planck-Kolmogorov Equation | https://openreview.net/forum?id=hJ1BaJ5ELp | [
"Zhanfeng Mo",
"Haosen Shi",
"Sinno Jialin Pan"
] | Spotlight | Neural pruning aims to compress and accelerate deep neural networks by identifying the optimal subnetwork within a specified sparsity budget. In this work, we study how to gradually sparsify the unpruned dense model to the target sparsity level with minimal performance drop. Specifically, we analyze the evolution of the population of optimal subnetworks under continuous sparsity increments from a thermodynamic perspective. We first reformulate neural pruning as an expected loss minimization problem over the mask distributions. Then, we establish an effective approximation for the sparsity evolution of the optimal mask distribution, termed the **S**parsity Evolutionary **F**okker-**P**lanck-**K**olmogorov Equation (**SFPK**), which provides closed-form, mathematically tractable guidance on distributional transitions for minimizing the expected loss under an infinitesimal sparsity increment. On top of that, we propose SFPK-pruner, a particle simulation-based probabilistic pruning method, to sample performant masks with desired sparsity from the destination distribution of SFPK. In theory, we establish the convergence guarantee for the proposed SFPK-pruner. Our SFPK-pruner exhibits competitive performance in various pruning scenarios. The code is available on https://github.com/mzf666/SFPK-main. | Optimization for Deep Network, Probabilistic Method, Machine learning, Model compression | We propose a principled probabilistic pruning framework, coined Sparsity Evolutionary FPK Equation that enables us to generate performant masks with desired sparsity via particle simulation of the dynamic of optimal mask distribution. | 5,759 | null |
Diffusion Attribution Score: Evaluating Training Data Influence in Diffusion Models | https://openreview.net/forum?id=kuutidLf6R | [
"Jinxu Lin",
"Linwei Tao",
"Minjing Dong",
"Chang Xu"
] | Spotlight | As diffusion models become increasingly popular, the misuse of copyrighted and private images has emerged as a major concern. One promising solution to mitigate this issue is identifying the contribution of specific training samples in generative models, a process known as data attribution. Existing data attribution methods for diffusion models typically quantify the contribution of a training sample by evaluating the change in diffusion loss when the sample is included or excluded from the training process.
However, we argue that the direct usage of diffusion loss cannot represent such a contribution accurately due to the calculation of diffusion loss.
Specifically, these approaches measure the divergence between predicted and ground truth distributions, which leads to an indirect comparison between the predicted distributions and cannot represent the variances between model behaviors.
To address these issues, we aim to measure the direct comparison between predicted distributions with an attribution score to analyse the training sample importance, which is achieved by Diffusion Attribution Score (\textit{DAS}).
Underpinned by rigorous theoretical analysis, we elucidate the effectiveness of DAS.
Additionally, we explore strategies to accelerate DAS calculations, facilitating its application to large-scale diffusion models.
Our extensive experiments across various datasets and diffusion models demonstrate that DAS significantly surpasses previous benchmarks in terms of the linear data-modelling score, establishing new state-of-the-art performance. | Diffusion Model; Data Attribution; Training Data Influence | We propose Diffusion Attribution Score in diffusion model which can be used to evaluate the influence of training sample in the generation. Our proposed method reached the State Of The Art performance compared with baseline method. | 5,713 | 2410.18639 |
Uncovering Gaps in How Humans and LLMs Interpret Subjective Language | https://openreview.net/forum?id=gye2U9uNXx | [
"Erik Jones",
"Arjun Patrawala",
"Jacob Steinhardt"
] | Spotlight | Humans often rely on subjective natural language to direct language models (LLMs); for example, users might instruct the LLM to write an *enthusiastic* blogpost, while developers might train models to be *helpful* and *harmless* using LLM-based edits. The LLM’s *operational semantics* of such subjective phrases---how it adjusts its behavior when each phrase is included in the prompt---thus dictates how aligned it is with human intent. In this work, we uncover instances of *misalignment* between LLMs' actual operational semantics and what humans expect. Our method, TED (thesaurus error detector), first constructs a thesaurus that captures whether two phrases have similar operational semantics according to the LLM. It then elicits failures by unearthing disagreements between this thesaurus and a human-constructed reference. TED routinely produces surprising instances of misalignment; for example, Mistral 7B Instruct produces more *harassing* outputs when it edits text to be *witty*, and Llama 3 8B Instruct produces *dishonest* articles when instructed to make the articles *enthusiastic*. Our results demonstrate that humans can uncover unexpected LLM behavior by scrutinizing relationships between abstract concepts, without supervising outputs directly. | safety, alignment, constitutional ai, language model failures, misalignment, automated evaluation, automated red-teaming | null | 5,672 | 2503.04113 |
Learning local equivariant representations for quantum operators | https://openreview.net/forum?id=kpq3IIjUD3 | [
"Zhanghao Zhouyin",
"Zixi Gan",
"Shishir Kumar Pandey",
"Linfeng Zhang",
"Qiangqiang Gu"
] | Spotlight | Predicting quantum operator matrices such as Hamiltonian, overlap, and density matrices in the density functional theory (DFT) framework is crucial for material science. Current methods often focus on individual operators and struggle with efficiency and scalability for large systems. Here we introduce a novel deep learning model, SLEM (strictly localized equivariant message-passing), for predicting multiple quantum operators that achieves state-of-the-art accuracy while dramatically improving computational efficiency. SLEM's key innovation is its strict locality-based design for equivariant representations of quantum tensors while preserving physical symmetries. This enables complex many-body dependency without expanding the effective receptive field, leading to superior data efficiency and transferability. Using an innovative SO(2) convolution and invariant overlap parameterization, SLEM reduces the computational complexity of high-order tensor products and is, therefore, capable of handling systems requiring the $f$ and $g$ orbitals in their basis sets. We demonstrate SLEM's capabilities across diverse 2D and 3D materials, achieving high accuracy even with limited training data. SLEM's design facilitates efficient parallelization, potentially extending DFT simulations to systems with device-level sizes, opening new possibilities for large-scale quantum simulations and high-throughput materials discovery. | Density Functional Theory, Local Graph Neural Network, Equivariant Neural Network | A Strictly Local Equivariant Neural Network for Density Functional Theory Hamiltonian/density matrix/overlap matrix learning, accelerated via invariant parameterization of overlap matrix and SO(2) convolution. | 5,661 | 2407.06053 |
PhyMPGN: Physics-encoded Message Passing Graph Network for spatiotemporal PDE systems | https://openreview.net/forum?id=fU8H4lzkIm | [
"Bocheng Zeng",
"Qi Wang",
"Mengtao Yan",
"Yang Liu",
"Ruizhi Chengze",
"Yi Zhang",
"Hongsheng Liu",
"Zidong Wang",
"Hao Sun"
] | Spotlight | Solving partial differential equations (PDEs) serves as a cornerstone for modeling complex dynamical systems. Recent progresses have demonstrated grand benefits of data-driven neural-based models for predicting spatiotemporal dynamics (e.g., tremendous speedup gain compared with classical numerical methods). However, most existing neural models rely on rich training data, have limited extrapolation and generalization abilities, and suffer to produce precise or reliable physical prediction under intricate conditions (e.g., irregular mesh or geometry, complex boundary conditions, diverse PDE parameters, etc.). To this end, we propose a new graph learning approach, namely, Physics-encoded Message Passing Graph Network (PhyMPGN), to model spatiotemporal PDE systems on irregular meshes given small training datasets. Specifically, we incorporate a GNN into a numerical integrator to approximate the temporal marching of spatiotemporal dynamics for a given PDE system. Considering that many physical phenomena are governed by diffusion processes, we further design a learnable Laplace block, which encodes the discrete Laplace-Beltrami operator, to aid and guide the GNN learning in a physically feasible solution space. A boundary condition padding strategy is also designed to improve the model convergence and accuracy. Extensive experiments demonstrate that PhyMPGN is capable of accurately predicting various types of spatiotemporal dynamics on coarse unstructured meshes, consistently achieves the state-of-the-art results, and outperforms other baselines with considerable gains. | Physics-encoded; Spatiotemporal PDEs; Graph Network; Deep Learning; | Presented a Physics-encoded Message Passing Graph Network for simulation of spatiotemporal PDE systems. | 5,640 | 2410.01337 |
Demystifying the Token Dynamics of Deep Selective State Space Models | https://openreview.net/forum?id=qtTIP5Gjc5 | [
"Thieu Vo",
"Duy-Tung Pham",
"Xin T. Tong",
"Tan Minh Nguyen"
] | Spotlight | Selective state space models (SSM), such as Mamba, have gained prominence for their effectiveness in modeling sequential data. Despite their outstanding empirical performance, a comprehensive theoretical understanding of deep selective SSM remains elusive, hindering their further development and adoption for applications that need high fidelity. In this paper, we investigate the dynamical properties of tokens in a pre-trained Mamba model. In particular, we derive the dynamical system governing the continuous-time limit of the Mamba model and characterize the asymptotic behavior of its solutions. In the one-dimensional case, we prove that only one of the following two scenarios happens: either all tokens converge to zero, or all tokens diverge to infinity. We provide criteria based on model parameters to determine when each scenario occurs. For the convergent scenario, we empirically verify that this scenario negatively impacts the model's performance. For the divergent scenario, we prove that different tokens will diverge to infinity at different rates, thereby contributing unequally to the updates during model training. Based on these investigations, we propose two refinements for the model: excluding the convergent scenario and reordering tokens based on their importance scores, both aimed at improving practical performance. Our experimental results validate these refinements, offering insights into enhancing Mamba's effectiveness in real-world applications. | Selective state-space model, continuous-time limit, dynamical system, asymptotic behavior, token reordering | We describe the dynamical properties of tokens in a deep selective state-space model, and based on that, we suggest improvements to the model by excluding negatively impactful scenarios and reordering tokens using importance scores. | 5,608 | 2410.03292 |
Let SSMs be ConvNets: State-space Modeling with Optimal Tensor Contractions | https://openreview.net/forum?id=PkpNRmBZ32 | [
"Yan Ru Pei"
] | Spotlight | We introduce Centaurus, a class of networks composed of generalized state-space model (SSM) blocks, where the SSM operations can be treated as tensor contractions during training. The optimal order of tensor contractions can then be systematically determined for every SSM block to maximize training efficiency. This allows more flexibility in designing SSM blocks beyond the depthwise-separable configuration commonly implemented. The new design choices will take inspiration from classical convolutional blocks including group convolutions, full convolutions, and bottleneck blocks. We architect the Centaurus network with a mixture of these blocks, to balance between network size and performance, as well as memory and computational efficiency during both training and inference. We show that this heterogeneous network design outperforms its homogeneous counterparts in raw audio processing tasks including keyword spotting, speech denoising, and automatic speech recognition (ASR). For ASR, Centaurus is the first network with competitive performance that can be made fully state-space based, without using any nonlinear recurrence (LSTMs), explicit convolutions (CNNs), or (surrogate) attention mechanism. | state-space models; convolution; tensor networks; audio processing; speech recognition | using deep SSMs like ConvNets to do audio processing | 5,569 | 2501.13230 |
MixEval-X: Any-to-any Evaluations from Real-world Data Mixture | https://openreview.net/forum?id=hpCfPEvBsr | [
"Jinjie Ni",
"Yifan Song",
"Deepanway Ghosal",
"Bo Li",
"David Junhao Zhang",
"Xiang Yue",
"Fuzhao Xue",
"Yuntian Deng",
"Zian Zheng",
"Kaichen Zhang",
"Mahir Shah",
"Kabir Jain",
"Yang You",
"Michael Shieh"
] | Spotlight | Perceiving and generating diverse modalities are crucial for AI models to effectively learn from and engage with real-world signals, necessitating reliable evaluations for their development. We identify two major issues in current evaluations: (1) inconsistent standards, shaped by different communities with varying protocols and maturity levels; and (2) significant query, grading, and generalization biases. To address these, we introduce MixEval-X, the first any-to-any, real-world benchmark designed to optimize and standardize evaluations across diverse input and output modalities. We propose multi-modal benchmark mixture and adaptation-rectification pipelines to reconstruct real-world task distributions, ensuring evaluations generalize effectively to real-world use cases. Extensive meta-evaluations show our approach effectively aligns benchmark samples with real-world task distributions. Meanwhile, MixEval-X's model rankings correlate strongly with that of crowd-sourced real-world evaluations (up to 0.98) while being much more efficient. We provide comprehensive leaderboards to rerank existing models and organizations and offer insights to enhance understanding of multi-modal evaluations and inform future research. | Evaluation, Multi-modal Evaluation, Benchmark, Multi-modal Benchmark, Any-to-any, MixEval, Real-world, Data Mixture, Artificial General Intelligence, AGI | We propose MixEval-X, the first any-to-any real-world benchmark optimizing benchmark mixtures for a wide range of input-output modalities. | 5,532 | null |
Knowledge Localization: Mission Not Accomplished? Enter Query Localization! | https://openreview.net/forum?id=tfyHbvFZ0K | [
"Yuheng Chen",
"Pengfei Cao",
"Yubo Chen",
"Kang Liu",
"Jun Zhao"
] | Spotlight | Large language models (LLMs) store extensive factual knowledge, but the mechanisms behind how they store and express this knowledge remain unclear.
The Knowledge Neuron (KN) thesis is a prominent theory for explaining these mechanisms. This theory is based on the **Knowledge Localization (KL)** assumption, which suggests that a fact can be localized to a few knowledge storage units, namely knowledge neurons.
However, this assumption has two limitations: first, it may be too rigid regarding knowledge storage, and second, it neglects the role of the attention module in knowledge expression.
In this paper, we first re-examine the KL assumption and demonstrate that its limitations do indeed exist. To address these, we then present two new findings, each targeting one of the limitations: one focusing on knowledge storage and the other on knowledge expression.
We summarize these findings as **Query Localization** assumption and argue that the KL assumption can be viewed as a simplification of the QL assumption.
Based on QL assumption, we further propose the Consistency-Aware KN modification method, which improves the performance of knowledge modification, further validating our new assumption. We conduct 39 sets of experiments, along with additional visualization experiments, to rigorously confirm our conclusions. Code will be made public soon. | Knowledge Neruon Thesis, Knowledge Localization, Query Localization | We re-evaluate the knowledge localization assumption, demonstrate its limitations, and propose the query localization assumption, proving that the knowledge localization assumption is merely a simplification of the query localization assumption. | 5,438 | 2405.14117 |
Graph Sparsification via Mixture of Graphs | https://openreview.net/forum?id=7ANDviElAo | [
"Guibin Zhang",
"Xiangguo Sun",
"Yanwei Yue",
"Chonghe Jiang",
"Kun Wang",
"Tianlong Chen",
"Shirui Pan"
] | Spotlight | Graph Neural Networks (GNNs) have demonstrated superior performance across various graph learning tasks but face significant computational challenges when applied to large-scale graphs. One effective approach to mitigate these challenges is graph sparsification, which involves removing non-essential edges to reduce computational overhead. However, previous graph sparsification methods often rely on a single global sparsity setting and uniform pruning criteria, failing to provide customized sparsification schemes for each node's complex local context.
In this paper, we introduce Mixture-of-Graphs (MoG), leveraging the concept of Mixture-of-Experts (MoE), to dynamically select tailored pruning solutions for each node. Specifically, MoG incorporates multiple sparsifier experts, each characterized by unique sparsity levels and pruning criteria, and selects the appropriate experts for each node. Subsequently, MoG performs a mixture of the sparse graphs produced by different experts on the Grassmann manifold to derive an optimal sparse graph. One notable property of MoG is its entirely local nature, as it depends on the specific circumstances of each individual node. Extensive experiments on four large-scale OGB datasets and two superpixel datasets, equipped with five GNN backbones, demonstrate that MoG (I) identifies subgraphs at higher sparsity levels ($8.67\\%\sim 50.85\\%$), with performance equal to or better than the dense graph, (II) achieves $1.47-2.62\times$ speedup in GNN inference with negligible performance drop, and (III) boosts ``top-student'' GNN performance ($1.02\\%\uparrow$ on RevGNN+\textsc{ogbn-proteins} and $1.74\\%\\uparrow$ on DeeperGCN+\textsc{ogbg-ppa}). The source code is available at \url{https://github.com/yanweiyue/MoG}. | Graph Sparsification, Mixture-of-Experts | null | 5,422 | 2405.14260 |
Realistic Evaluation of Deep Partial-Label Learning Algorithms | https://openreview.net/forum?id=FtX6oAW7Dd | [
"Wei Wang",
"Dong-Dong Wu",
"Jindong Wang",
"Gang Niu",
"Min-Ling Zhang",
"Masashi Sugiyama"
] | Spotlight | Partial-label learning (PLL) is a weakly supervised learning problem in which
each example is associated with multiple candidate labels and only one is the
true label. In recent years, many deep PLL algorithms have been developed to
improve model performance. However, we find that some early developed
algorithms are often underestimated and can outperform many later algorithms
with complicated designs. In this paper, we delve into the empirical
perspective of PLL and identify several critical but previously overlooked
issues. First, model selection for PLL is non-trivial, but has never been
systematically studied. Second, the experimental settings are highly
inconsistent, making it difficult to evaluate the effectiveness of the
algorithms. Third, there is a lack of real-world image datasets that can be
compatible with modern network architectures. Based on these findings, we
propose PLENCH, the first Partial-Label learning bENCHmark to systematically
compare state-of-the-art deep PLL algorithms. We investigate the model
selection problem for PLL for the first time, and propose novel model selection
criteria with theoretical guarantees. We also create Partial-Label CIFAR-10
(PLCIFAR10), an image dataset of human-annotated partial labels collected from
Amazon Mechanical Turk, to provide a testbed for evaluating the performance of
PLL algorithms in more realistic scenarios. Researchers can quickly and
conveniently perform a comprehensive and fair evaluation and verify the
effectiveness of newly developed algorithms based on PLENCH. We hope that
PLENCH will facilitate standardized, fair, and practical evaluation of PLL
algorithms in the future. | Partial-label learning, weakly supervised learning, benchmark. | The first partial-label learning benchmark with a new dataset of human-annotated partial labels. | 5,419 | 2502.10184 |
BodyGen: Advancing Towards Efficient Embodiment Co-Design | https://openreview.net/forum?id=cTR17xl89h | [
"Haofei Lu",
"Zhe Wu",
"Junliang Xing",
"Jianshu Li",
"Ruoyu Li",
"Zhe Li",
"Yuanchun Shi"
] | Spotlight | Embodiment co-design aims to optimize a robot's morphology and control policy simultaneously.
While prior work has demonstrated its potential for generating environment-adaptive robots, this field still faces persistent challenges in optimization efficiency due to the (i) combinatorial nature of morphological search spaces and (ii) intricate dependencies between morphology and control.
We prove that the ineffective morphology representation and unbalanced reward signals between the design and control stages are key obstacles to efficiency.
To advance towards efficient embodiment co-design, we propose **BodyGen**, which utilizes (1) topology-aware self-attention for both design and control, enabling efficient morphology representation with lightweight model sizes; (2) a temporal credit assignment mechanism that ensures balanced reward signals for optimization. With our findings, BodyGen achieves an average **60.03%** performance improvement against state-of-the-art baselines. We provide codes and more results on the website: https://genesisorigin.github.io. | Reinforcement Learning | This paper introduces BodyGen: a novel framework for efficient embodiment co-design. | 5,415 | 2503.00533 |
RAG-SR: Retrieval-Augmented Generation for Neural Symbolic Regression | https://openreview.net/forum?id=NdHka08uWn | [
"Hengzhe Zhang",
"Qi Chen",
"Bing XUE",
"Wolfgang Banzhaf",
"Mengjie Zhang"
] | Spotlight | Symbolic regression is a key task in machine learning, aiming to discover mathematical expressions that best describe a dataset. While deep learning has increased interest in using neural networks for symbolic regression, many existing approaches rely on pre-trained models. These models require significant computational resources and struggle with regression tasks involving unseen functions and variables. A pre-training-free paradigm is needed to better integrate with search-based symbolic regression algorithms. To address these limitations, we propose a novel framework for symbolic regression that integrates evolutionary feature construction with a neural network, without the need for pre-training. Our approach adaptively generates symbolic trees that align with the desired semantics in real-time using a language model trained via online supervised learning, providing effective building blocks for feature construction. To mitigate hallucinations from the language model, we design a retrieval-augmented generation mechanism that explicitly leverages searched symbolic expressions. Additionally, we introduce a scale-invariant data augmentation technique that further improves the robustness and generalization of the model. Experimental results demonstrate that our framework achieves state-of-the-art accuracy across 25 regression algorithms and 120 regression tasks. | Symbolic Regression, Genetic Programming, Transformers, Deep Learning | null | 5,410 | null |
Theory on Mixture-of-Experts in Continual Learning | https://openreview.net/forum?id=7XgKAabsPp | [
"Hongbo Li",
"Sen Lin",
"Lingjie Duan",
"Yingbin Liang",
"Ness Shroff"
] | Spotlight | Continual learning (CL) has garnered significant attention because of its ability to adapt to new tasks that arrive over time. Catastrophic forgetting (of old tasks) has been identified as a major issue in CL, as the model adapts to new tasks. The Mixture-of-Experts (MoE) model has recently been shown to effectively mitigate catastrophic forgetting in CL, by employing a gating network to sparsify and distribute diverse tasks among multiple experts. However, there is a lack of theoretical analysis of MoE and its impact on the learning performance in CL. This paper provides the first theoretical results to characterize the impact of MoE in CL via the lens of overparameterized linear regression tasks. We establish the benefit of MoE over a single expert by proving that the MoE model can diversify its experts to specialize in different tasks, while its router learns to select the right expert for each task and balance the loads across all experts. Our study further suggests an intriguing fact that the MoE in CL needs to terminate the update of the gating network after sufficient training rounds to attain system convergence, which is not needed in the existing MoE studies that do not consider the continual task arrival. Furthermore, we provide explicit expressions for the expected forgetting and overall generalization error to characterize the benefit of MoE in the learning performance in CL. Interestingly, adding more experts requires additional rounds before convergence, which may not enhance the learning performance. Finally, we conduct experiments on both synthetic and real datasets to extend these insights from linear models to deep neural networks (DNNs), which also shed light on the practical algorithm design for MoE in CL. | continual learning, mixture-of-experts, catastrophic forgetting, generalization error | null | 5,392 | 2406.16437 |
Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees | https://openreview.net/forum?id=cznqgb4DNv | [
"Shahryar Zehtabi",
"Dong-Jun Han",
"Rohit Parasnis",
"Seyyedali Hosseinalipour",
"Christopher Brinton"
] | Spotlight | Decentralized federated learning (DFL) captures FL settings where both (i) model updates and (ii) model aggregations are exclusively carried out by the clients without a central server. Existing DFL works have mostly focused on settings where clients conduct a fixed number of local updates between local model exchanges, overlooking heterogeneity and dynamics in communication and computation capabilities. In this work, we propose Decentralized Sporadic Federated Learning ($\texttt{DSpodFL}$), a DFL methodology built on a generalized notion of *sporadicity* in both local gradient and aggregation processes. $\texttt{DSpodFL}$ subsumes many existing decentralized optimization methods under a unified algorithmic framework by modeling the per-iteration (i) occurrence of gradient descent at each client and (ii) exchange of models between client pairs as arbitrary indicator random variables, thus capturing *heterogeneous and time-varying* computation/communication scenarios. We analytically characterize the convergence behavior of $\texttt{DSpodFL}$ for both convex and non-convex models and for both constant and diminishing learning rates, under mild assumptions on the communication graph connectivity, data heterogeneity across clients, and gradient noises. We show how our bounds recover existing results from decentralized gradient descent as special cases. Experiments demonstrate that $\texttt{DSpodFL}$ consistently achieves improved training speeds compared with baselines under various system settings. | Decentralized Federated Learning, Sporadicity, Unified Algorithmic Framework, Convergence Analysis | null | 5,391 | 2402.03448 |
MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL | https://openreview.net/forum?id=6RtRsg8ZV1 | [
"Claas A Voelcker",
"Marcel Hussing",
"Eric Eaton",
"Amir-massoud Farahmand",
"Igor Gilitschenski"
] | Spotlight | Building deep reinforcement learning (RL) agents that find a good policy with few samples has proven notoriously challenging. To achieve sample efficiency, recent work has explored updating neural networks with large numbers of gradient steps for every new sample. While such high update-to-data (UTD) ratios have shown strong empirical performance, they also introduce instability to the training process. Previous approaches need to rely on periodic neural network parameter resets to address this instability, but restarting the training process is infeasible in many real-world applications and requires tuning the resetting interval. In this paper, we focus on one of the core difficulties of stable training with limited samples: the inability of learned value functions to generalize to unobserved on-policy actions. We mitigate this issue directly by augmenting the off-policy RL training process with a small amount of data generated from a learned world model. Our method, Model-Augmented Data for TD Learning (MAD-TD) uses small amounts of generated data to stabilize high UTD training and achieve competitive performance on the most challenging tasks in the DeepMind control suite. Our experiments further highlight the importance of employing a good model to generate data, MAD-TD's ability to combat value overestimation, and its practical stability gains for continued learning. | reinforcement learning, model based reinforcement learning, data augmentation, high update ratios | Using model generated data can lead to stable learning with high update ratios in off-policy RL | 5,371 | null |
Linear Mode Connectivity in Differentiable Tree Ensembles | https://openreview.net/forum?id=UqYNPyotxL | [
"Ryuichi Kanoh",
"Mahito Sugiyama"
] | Spotlight | Linear Mode Connectivity (LMC) refers to the phenomenon that performance remains consistent for linearly interpolated models in the parameter space. For independently optimized model pairs from different random initializations, achieving LMC is considered crucial for understanding the stable success of the non-convex optimization in modern machine learning models and for facilitating practical parameter-based operations such as model merging. While LMC has been achieved for neural networks by considering the permutation invariance of neurons in each hidden layer, its attainment for other models remains an open question. In this paper, we first achieve LMC for soft tree ensembles, which are tree-based differentiable models extensively used in practice. We show the necessity of incorporating two invariances: subtree flip invariance and splitting order invariance, which do not exist in neural networks but are inherent to tree architectures, in addition to permutation invariance of trees. Moreover, we demonstrate that it is even possible to exclude such additional invariances while keeping LMC by designing decision list-based tree architectures, where such invariances do not exist by definition. Our findings indicate the significance of accounting for architecture-specific invariances in achieving LMC. | Linear Mode Connectivity, Soft Tree | Considering additional invariances beyond tree permutation, we achieve linear mode connectivity for tree ensembles. | 5,345 | 2405.14596 |
Overcoming False Illusions in Real-World Face Restoration with Multi-Modal Guided Diffusion Model | https://openreview.net/forum?id=m9RNBZewW2 | [
"Keda TAO",
"Jinjin Gu",
"Yulun Zhang",
"Xiucheng Wang",
"Nan Cheng"
] | Spotlight | We introduce a novel Multi-modal Guided Real-World Face Restoration (MGFR) technique designed to improve the quality of facial image restoration from low-quality inputs. Leveraging a blend of attribute text prompts, high-quality reference images, and identity information, MGFR can mitigate the generation of false facial attributes and identities often associated with generative face restoration methods. By incorporating a dual-control adapter and a two-stage training strategy, our method effectively utilizes multi-modal prior information for targeted restoration tasks. We also present the Reface-HQ dataset, comprising over 23,000 high-resolution facial images across 5,000 identities, to address the need for reference face training images. Our approach achieves superior visual quality in restoring facial details under severe degradation and allows for controlled restoration processes, enhancing the accuracy of identity preservation and attribute correction. Including negative quality samples and attribute prompts in the training further refines the model's ability to generate detailed and perceptually accurate images. | Face image restoration, diffusion model | null | 5,312 | 2410.04161 |
DEEM: Diffusion models serve as the eyes of large language models for image perception | https://openreview.net/forum?id=qtWjSboqfe | [
"Run Luo",
"Yunshui Li",
"Longze Chen",
"Wanwei He",
"Ting-En Lin",
"Ziqiang Liu",
"Lei Zhang",
"Zikai Song",
"Hamid Rokny",
"Xiaobo Xia",
"Tongliang Liu",
"Binyuan Hui",
"Min Yang"
] | Spotlight | The development of large language models (LLMs) has significantly advanced the emergence of large multimodal models (LMMs). While LMMs have achieved tremendous success by promoting the synergy between multimodal comprehension and creation, they often face challenges when confronted with out-of-distribution data, such as which can hardly distinguish orientation, quantity, color, structure, etc. This is primarily due to their reliance on image encoders trained to encode images into task-relevant features, which may lead them to disregard irrelevant details. Delving into the modeling capabilities of diffusion models for images naturally prompts the question: Can diffusion models serve as the eyes of large language models for image perception? In this paper, we propose DEEM, a simple but effective approach that utilizes the generative feedback of diffusion models to align the semantic distributions of the image encoder. This addresses the drawbacks of previous methods that solely relied on image encoders like CLIP-ViT, thereby enhancing the model's resilience against out-of-distribution samples and reducing visual hallucinations. Importantly, this is achieved without requiring additional training modules and with fewer training parameters. We extensively evaluated DEEM on both our newly constructed RobustVQA benchmark and other well-known benchmarks, POPE and MMVP, for visual hallucination and perception. In particular, DEEM improves LMM's visual perception performance to a large extent (e.g., 4\% ↑ on RobustVQA, 6.5\% ↑ on MMVP and 12.8 \% ↑ on POPE ). Compared to the state-of-the-art interleaved content generation models, DEEM exhibits enhanced robustness and a superior capacity to alleviate model hallucinations while utilizing fewer trainable parameters, less pre-training data (10\%), and a smaller base model size. Extensive experiments demonstrate that DEEM enhances the performance of LMMs on various downstream tasks without inferior performance in the long term, including visual question answering, image captioning, and text-conditioned image synthesis. | MLLM; Diffusion Model; | Diffusion Model Can help MLLM see better | 5,298 | 2405.15232 |
Stabilizing Reinforcement Learning in Differentiable Multiphysics Simulation | https://openreview.net/forum?id=DRiLWb8bJg | [
"Eliot Xing",
"Vernon Luk",
"Jean Oh"
] | Spotlight | Recent advances in GPU-based parallel simulation have enabled practitioners to collect large amounts of data and train complex control policies using deep reinforcement learning (RL), on commodity GPUs. However, such successes for RL in robotics have been limited to tasks sufficiently simulated by fast rigid-body dynamics. Simulation techniques for soft bodies are comparatively several orders of magnitude slower, thereby limiting the use of RL due to sample complexity requirements. To address this challenge, this paper presents both a novel RL algorithm and a simulation platform to enable scaling RL on tasks involving rigid bodies and deformables. We introduce Soft Analytic Policy Optimization (SAPO), a maximum entropy first-order model-based actor-critic RL algorithm, which uses first-order analytic gradients from differentiable simulation to train a stochastic actor to maximize expected return and entropy. Alongside our approach, we develop Rewarped, a parallel differentiable multiphysics simulation platform that supports simulating various materials beyond rigid bodies. We re-implement challenging manipulation and locomotion tasks in Rewarped, and show that SAPO outperforms baselines over a range of tasks that involve interaction between rigid bodies, articulations, and deformables. Additional details at https://rewarped.github.io/. | reinforcement learning, differentiable simulation | null | 5,094 | 2412.12089 |
Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification | https://openreview.net/forum?id=cv2iMNWCsh | [
"Kaizheng Wang",
"Fabio Cuzzolin",
"Keivan Shariatmadar",
"David Moens",
"Hans Hallez"
] | Spotlight | This paper presents an innovative approach, called credal wrapper, to formulating a credal set representation of model averaging for Bayesian neural networks (BNNs) and deep ensembles (DEs), capable of improving uncertainty estimation in classification tasks. Given a finite collection of single predictive distributions derived from BNNs or DEs, the proposed credal wrapper approach extracts an upper and a lower probability bound per class, acknowledging the epistemic uncertainty due to the availability of a limited amount of distributions. Such probability intervals over classes can be mapped on a convex set of probabilities (a credal set) from which, in turn, a unique prediction can be obtained using a transformation called intersection probability transformation. In this article, we conduct extensive experiments on several out-of-distribution (OOD) detection benchmarks, encompassing various dataset pairs (CIFAR10/100 vs SVHN/Tiny-ImageNet, CIFAR10 vs CIFAR10-C, CIFAR100 vs CIFAR100-C and ImageNet vs ImageNet-O) and using different network architectures (such as VGG16, ResNet-18/50, EfficientNet B2, and ViT Base). Compared to the BNN and DE baselines, the proposed credal wrapper method exhibits superior performance in uncertainty estimation and achieves a lower expected calibration error on corrupted data. | Uncertainty Estimation, Model Averaging, Credal Stes, Probability Intervals, Out-of-Distribution Detection | null | 5,087 | null |
Discovering Temporally Compositional Neural Manifolds with Switching Infinite GPFA | https://openreview.net/forum?id=2iCIHgE8KG | [
"Changmin Yu",
"Maneesh Sahani",
"Máté Lengyel"
] | Spotlight | Gaussian Process Factor Analysis (GPFA) is a powerful latent variable model for extracting low-dimensional manifolds underlying population neural activities. However, one limitation of standard GPFA models is that the number of latent factors needs to be pre-specified or selected through heuristic-based processes, and that all factors contribute at all times. We propose the infinite GPFA model, a fully Bayesian non-parametric extension of the classical GPFA by incorporating an Indian Buffet Process (IBP) prior over the factor loading process, such that it is possible to infer a potentially infinite set of latent factors, and the identity of those factors that contribute to neural firings in a compositional manner at \textit{each} time point. Learning and inference in the infinite GPFA model is performed through variational expectation-maximisation, and we additionally propose scalable extensions based on sparse variational Gaussian Process methods. We empirically demonstrate that the infinite GPFA model correctly infers dynamically changing activations of latent factors on a synthetic dataset. By fitting the infinite GPFA model to population activities of hippocampal place cells during spatial tasks with alternating random foraging and spatial memory phases, we identify novel non-trivial and behaviourally meaningful dynamics in the neural encoding process. | Computational neuroscience, neural data analysis, Bayesian nonparametrics, latent variable modelling; | We propose a fully Bayesian nonparametric extension of GPFA that enables discovery of temporally compositional neural manifolds underlying high-dimensional population neuronal activities. | 5,086 | null |
Presto! Distilling Steps and Layers for Accelerating Music Generation | https://openreview.net/forum?id=Gj5JTAwdoy | [
"Zachary Novack",
"Ge Zhu",
"Jonah Casebeer",
"Julian McAuley",
"Taylor Berg-Kirkpatrick",
"Nicholas J. Bryan"
] | Spotlight | Despite advances in diffusion-based text-to-music (TTM) methods, efficient, high-quality generation remains a challenge. We introduce Presto!, an approach to inference acceleration for score-based diffusion transformers via reducing both sampling steps and cost per step. To reduce steps, we develop a new score-based distribution matching distillation (DMD) method for the EDM-family of diffusion models, the first GAN-based distillation method for TTM. To reduce the cost per step, we develop a simple, but powerful improvement to a recent layer distillation method that improves learning via better preserving hidden state variance. Finally, we combine our step and layer distillation methods together for a dual-faceted approach. We evaluate our step and layer distillation methods independently and show each yield best-in-class performance. Our combined distillation method can generate high-quality outputs with improved diversity, accelerating our base model by 10-18x (230/435ms latency for 32 second mono/stereo 44.1kHz, 15x faster than the comparable SOTA model) — the fastest TTM to our knowledge. | music generation, diffusion distillation, diffusion, diffusion acceleration, text-to-music generation, layer dropping | null | 4,966 | 2410.05167 |
Grounding Video Models to Actions through Goal Conditioned Exploration | https://openreview.net/forum?id=G6dMvRuhFr | [
"Yunhao Luo",
"Yilun Du"
] | Spotlight | Large video models, pretrained on massive quantities of amount of Internet video, provide a rich source of physical knowledge about the dynamics and motions of objects and tasks.
However, video models are not grounded in the embodiment of an agent, and do not describe how to actuate the world to reach the visual states depicted in a video.
To tackle this problem, current methods use a separate vision-based inverse dynamic model trained on embodiment-specific data to map image states to actions.
Gathering data to train such a model is often expensive and challenging, and this model is limited to visual settings similar to the ones in which data is available.
In this paper, we investigate how to directly ground video models to continuous actions through self-exploration in the embodied environment -- using generated video states as visual goals for exploration.
We propose a framework that uses trajectory level action generation in combination with video guidance to
enable an agent to solve complex tasks without any external supervision, e.g., rewards, action labels, or segmentation masks.
We validate the proposed approach on 8 tasks in Libero, 6 tasks in MetaWorld, 4 tasks in Calvin, and 12 tasks in iThor Visual Navigation.
We show how our approach is on par with or even surpasses multiple behavior cloning baselines trained on expert demonstrations while without requiring any action annotations. | Embodied AI, Decision Making, Robotics, Video Model | We illustrate how we can ground video models to actions without using actions labels through goal conditioned exploration. | 4,940 | 2411.07223 |
RESuM: A Rare Event Surrogate Model for Physics Detector Design | https://openreview.net/forum?id=lqTILjL6lP | [
"Ann-Kathrin Schuetz",
"A.W.P. Poon",
"Aobo Li"
] | Spotlight | The experimental discovery of neutrinoless double-beta decay (NLDBD) would answer one of the most important questions in physics: Why is there more matter than antimatter in our universe? To maximize the chances of discovery, NLDBD experiments must optimize their detector designs to minimize the probability of background events contaminating the detector. Given that this probability is inherently low, design optimization either requires extremely costly simulations to generate sufficient background counts or contending with significant variance. In this work, we formalize this dilemma as a Rare Event Design (RED) problem: identifying optimal design parameters when the design metric to be minimized is inherently small. We then designed the Rare Event Surrogate Model (RESuM) for physics detector design optimization under RED conditions. RESuM uses a pre-trained Conditional Neural Process (CNP) model to incorporate additional prior knowledge into a Multi-Fidelity Gaussian Process model. We applied RESuM to optimize neutron shielding designs for the LEGEND NLDBD experiment, identifying an optimal design that reduces the neutron background by $(66.5 \pm 3.5)$% while using only 3.3% of the computational resources compared to traditional methods. Given the prevalence of RED problems in other fields of physical sciences, especially in rare-event searches, the RESuM algorithm has broad potential for accelerating simulation-intensive applications. | surrogate model, simulation, rare event search, AI4Sci, AI for physics, conditional neural process, Bayesian methods, emulator, Multi-Fidelity Gaussian Process | We developed a Rare Event Surrogate Model (RESuM) powered by Conditional Neural Process to optimize particle physics detector design under high-variance design metrics. | 4,920 | null |
Bi-Factorial Preference Optimization: Balancing Safety-Helpfulness in Language Models | https://openreview.net/forum?id=GjM61KRiTG | [
"Wenxuan Zhang",
"Philip Torr",
"Mohamed Elhoseiny",
"Adel Bibi"
] | Spotlight | Fine-tuning large language models (LLMs) on human preferences, typically through reinforcement learning from human feedback (RLHF), has proven successful in enhancing their capabilities. However, ensuring the safety of LLMs during fine-tuning remains a critical concern, and mitigating the potential conflicts in safety and helpfulness is costly in RLHF. To address this issue, we propose a supervised learning framework called Bi-Factorial Preference Optimization (BFPO), which re-parameterizes a joint RLHF objective of both safety and helpfulness into a single supervised learning objective. In the supervised optimization, a labeling function is used to capture global preferences ranking to balance both safety and helpfulness. To evaluate BFPO, we develop a benchmark including comprehensive discriminative and generative tasks for helpfulness and harmlessness. The results indicate that our method significantly outperforms existing approaches in both safety and helpfulness. Moreover, BFPO eliminates the need for human prompting and annotation in LLM fine-tuning while achieving the same level of safety as methods that heavily rely on human labor, with less than 10\% of the computational resources. The training recipes and models will be released. | Large Language Models, RLHF, Safety | We introduce a theoretically framework to re-parameterize the multi-objective RLHF into supervised optimization and empirically show the effectiveness in improving both the helpfulness and harmlessness in LLM. | 4,919 | 2408.15313 |
Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems | https://openreview.net/forum?id=twEvvkQqPS | [
"Yunyang Li",
"Zaishuo Xia",
"Lin Huang",
"Xinran Wei",
"Samuel Harshe",
"Han Yang",
"Erpai Luo",
"Zun Wang",
"Jia Zhang",
"Chang Liu",
"Bin Shao",
"Mark Gerstein"
] | Spotlight | Density Functional Theory (DFT) is a pivotal method within quantum chemistry and materials science, with its core involving the construction and solution of the Kohn-Sham Hamiltonian. Despite its importance, the application of DFT is frequently limited by the substantial computational resources required to construct the Kohn-Sham Hamiltonian. In response to these limitations, current research has employed deep-learning models to efficiently predict molecular and solid Hamiltonians, with roto-translational symmetries encoded in their neural networks. However, the scalability of prior models may be problematic when applied to large molecules, resulting in non-physical predictions of ground-state properties. In this study, we generate a substantially larger training set (PubChemQH) than used previously and use it to create a scalable model for DFT calculations with physical accuracy. For our model, we introduce a loss function derived from physical principles, which we call Wavefunction Alignment Loss (WALoss). WALoss involves performing a basis change on the predicted Hamiltonian to align it with the observed one; thus, the resulting differences can serve as a surrogate for orbital energy differences, allowing models to make better predictions for molecular orbitals and total energies than previously possible. WALoss also substantially accelerates self-consistent-field (SCF) DFT calculations. Here, we show it achieves a reduction in total energy prediction error by a factor of 1347 and an SCF calculation speed-up by a factor of 18\%. These substantial improvements set new benchmarks for achieving accurate and applicable predictions in larger molecular systems. | AI for Science, Quantum Chemistry, EGNN | We present a new framework to enhance the scalability and applicability of Kohn-Shan Hamiltonian. | 4,886 | 2502.19227 |
Dense Video Object Captioning from Disjoint Supervision | https://openreview.net/forum?id=auZZ2gN0ZN | [
"Xingyi Zhou",
"Anurag Arnab",
"Chen Sun",
"Cordelia Schmid"
] | Spotlight | We propose a new task and model for dense video object captioning -- detecting, tracking and captioning trajectories of objects in a video. This task unifies spatial and temporal localization in video, whilst also requiring fine-grained visual understanding that is best described by natural language. We propose a unified model, and demonstrate how our end-to-end approach is more accurate and temporally coherent than a multi-stage pipeline combining state-of-the-art detection, tracking, and captioning models. Moreover, we propose a training strategy based on a mixture of disjoint tasks, which allows us to leverage diverse, large-scale datasets which supervise different parts of our model. Although each pretraining task only provides weak supervision, they are complementary and, when combined, result in noteworthy zero-shot ability and serve as strong initialization for additional finetuning to further improve accuracy. We carefully design new metrics capturing all components of our task, and show how we can repurpose existing video grounding datasets (e.g. VidSTG and VLN) for our new task. We show that our model improves upon a number of strong baselines for this new task. Furthermore, we can apply our model to the task of spatial grounding, outperforming prior state-of-the-art on VidSTG and VLN, without explicitly training for it. Our code is available at https://github.com/google-research/scenic. | object captioning, video, tracking | null | 4,872 | 2306.11729 |
SplatFormer: Point Transformer for Robust 3D Gaussian Splatting | https://openreview.net/forum?id=9NfHbWKqMF | [
"Yutong Chen",
"Marko Mihajlovic",
"Xiyi Chen",
"Yiming Wang",
"Sergey Prokudin",
"Siyu Tang"
] | Spotlight | 3D Gaussian Splatting (3DGS) has recently transformed photorealistic reconstruction, achieving high visual fidelity and real-time performance. However, rendering quality significantly deteriorates when test views deviate from the camera angles used during training, posing a major challenge for applications in immersive free-viewpoint rendering and navigation. In this work, we conduct a comprehensive evaluation of 3DGS and related novel view synthesis methods under out-of-distribution (OOD) test camera scenarios. By creating diverse test cases with synthetic and real-world datasets, we demonstrate that most existing methods, including those incorporating various regularization techniques and data-driven priors, struggle to generalize effectively to OOD views. To address this limitation, we introduce SplatFormer, the first point transformer model specifically designed to operate on Gaussian splats. SplatFormer takes as input an initial 3DGS set optimized under limited training views and refines it in a single forward pass, effectively removing potential artifacts in OOD test views. To our knowledge, this is the first successful application of point transformers directly on 3DGS sets, surpassing the limitations of previous multi-scene training methods, which could handle only a restricted number of input views during inference. Our model significantly improves rendering quality under extreme novel views, achieving state-of-the-art performance in these challenging scenarios and outperforming various 3DGS regularization techniques, multi-scene models tailored for sparse view synthesis, and diffusion-based frameworks. The project url is https://sergeyprokudin.github.io/splatformer. | Novel View Synthesis, Gaussian Splatting, Point cloud modeling | A feed-forward transformer that refines 3DGS for out-of-distribution novel view synthesis | 4,834 | 2411.06390 |
DeLLMa: Decision Making Under Uncertainty with Large Language Models | https://openreview.net/forum?id=Acvo2RGSCy | [
"Ollie Liu",
"Deqing Fu",
"Dani Yogatama",
"Willie Neiswanger"
] | Spotlight | The potential of large language models (LLMs) as decision support tools is increasingly being explored in fields such as business, engineering, and medicine, which often face challenging tasks of *decision-making under uncertainty*. In this paper, we show that directly prompting LLMs on these types of decision-making problems can yield poor results, especially as the problem complexity increases. To aid in these tasks, we propose DeLLMa (Decision-making Large Language Model assistant), a framework designed to enhance decision-making accuracy in uncertain environments. DeLLMa involves a multi-step reasoning procedure that integrates recent best practices in scaling *inference-time reasoning*, drawing upon principles from decision theory and utility theory, to provide an accurate and human-auditable decision-making process. We validate our procedure on multiple realistic decision-making environments, demonstrating that DeLLMa can consistently enhance the decision-making performance of leading language models, and achieve up to a 40% increase in accuracy over competing methods. Additionally, we show how performance improves when scaling compute at test time, and carry out human evaluations to benchmark components of DeLLMa. | large language models, decision theory, decision making under uncertainty | We introduce an inference-time reasoning procedure for reliable decision making under uncertainty with LLMs, drawing upon principles from classical decision theory. | 4,817 | 2402.02392 |
OmniRe: Omni Urban Scene Reconstruction | https://openreview.net/forum?id=11xgiMEI5o | [
"Ziyu Chen",
"Jiawei Yang",
"Jiahui Huang",
"Riccardo de Lutio",
"Janick Martinez Esturo",
"Boris Ivanovic",
"Or Litany",
"Zan Gojcic",
"Sanja Fidler",
"Marco Pavone",
"Li Song",
"Yue Wang"
] | Spotlight | We introduce OmniRe, a comprehensive system for efficiently creating high-fidelity digital twins of dynamic real-world scenes from on-device logs. Recent methods using neural fields or Gaussian Splatting primarily focus on vehicles, hindering a holistic framework for all dynamic foregrounds demanded by downstream applications, e.g., the simulation of human behavior. OmniRe extends beyond vehicle modeling to enable accurate, full-length reconstruction of diverse dynamic objects in urban scenes. Our approach builds scene graphs on 3DGS and constructs multiple Gaussian representations in canonical spaces that model various dynamic actors, including vehicles, pedestrians, cyclists, and others. OmniRe allows holistically reconstructing any dynamic object in the scene, enabling advanced simulations (~60 Hz) that include human-participated scenarios, such as pedestrian behavior simulation and human-vehicle interaction. This comprehensive simulation capability is unmatched by existing methods. Extensive evaluations on the Waymo dataset show that our approach outperforms prior state-of-the-art methods quantitatively and qualitatively by a large margin. We further extend our results to 5 additional popular driving datasets to demonstrate its generalizability on common urban scenes. Code and results are available at [omnire](https://ziyc.github.io/omnire/). | Gaussians Splatting, Neural Rendering, Dynamic Scene Reconstruction, Autonomous Driving | null | 4,816 | 2408.16760 |
ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish | https://openreview.net/forum?id=oCHsDpyawq | [
"Jan-Matthis Lueckmann",
"Alexander Immer",
"Alex Bo-Yuan Chen",
"Peter H. Li",
"Mariela D Petkova",
"Nirmala A Iyer",
"Luuk Willem Hesselink",
"Aparna Dev",
"Gudrun Ihrke",
"Woohyun Park",
"Alyson Petruncio",
"Aubrey Weigel",
"Wyatt Korff",
"Florian Engert",
"Jeff Lichtman",
"Misha Ahrens",
"Michal Januszewski",
"Viren Jain"
] | Spotlight | Data-driven benchmarks have led to significant progress in key scientific modeling domains including weather and structural biology. Here, we introduce the Zebrafish Activity Prediction Benchmark (ZAPBench) to measure progress on the problem of predicting cellular-resolution neural activity throughout an entire vertebrate brain. The benchmark is based on a novel dataset containing 4d light-sheet microscopy recordings of over 70,000 neurons in a larval zebrafish brain, along with motion stabilized and voxel-level cell segmentations of these data that facilitate development of a variety of forecasting methods. Initial results from a selection of time series and volumetric video modeling approaches achieve better performance than naive baseline methods, but also show room for further improvement. The specific brain used in the activity recording is also undergoing synaptic-level anatomical mapping, which will enable future integration of detailed structural information into forecasting methods. | neuroscience, zebrafish, forecasting, benchmark, timeseries, lightsheet microscopy, calcium imaging | ZAPBench evaluates how well different models can predict the activity of over 70,000 neurons in a novel larval zebrafish dataset. | 4,784 | 2503.02618 |
Lumina-T2X: Scalable Flow-based Large Diffusion Transformer for Flexible Resolution Generation | https://openreview.net/forum?id=EbWf36quzd | [
"Peng Gao",
"Le Zhuo",
"Dongyang Liu",
"Ruoyi Du",
"Xu Luo",
"Longtian Qiu",
"Yuhang Zhang",
"Rongjie Huang",
"Shijie Geng",
"Renrui Zhang",
"Junlin Xie",
"Wenqi Shao",
"Zhengkai Jiang",
"Tianshuo Yang",
"Weicai Ye",
"Tong He",
"Jingwen He",
"Junjun He",
"Yu Qiao",
"Hongsheng Li"
] | Spotlight | Sora unveils the potential of scaling Diffusion Transformer (DiT) for generating photorealistic images and videos at arbitrary resolutions, aspect ratios, and durations, yet it still lacks sufficient implementation details. In this paper, we introduce the Lumina-T2X family -- a series of Flow-based Large Diffusion Transformers (Flag-DiT) equipped with zero-initialized attention, as a simple and scalable generative framework that can be adapted to various modalities, e.g., transforming noise into images, videos, multi-view 3D objects, or audio clips conditioned on text instructions. By tokenizing the latent spatial-temporal space and incorporating learnable placeholders such as |[nextline]| and |[nextframe]| tokens, Lumina-T2X seamlessly unifies the representations of different modalities across various spatial-temporal resolutions. Advanced techniques like RoPE, KQ-Norm, and flow matching enhance the stability, flexibility, and scalability of Flag-DiT, enabling models of Lumina-T2X to scale up to 7 billion parameters and extend the context window to 128K tokens. This is particularly beneficial for creating ultra-high-definition images with our Lumina-T2I model and long 720p videos with our Lumina-T2V model. Remarkably, Lumina-T2I, powered by a 5-billion-parameter Flag-DiT, requires only 35% of the training computational costs of a 600-million-parameter naive DiT (PixArt-alpha), indicating that increasing the number of parameters significantly accelerates convergence of generative models without compromising visual quality. Our further comprehensive analysis underscores Lumina-T2X's preliminary capability in resolution extrapolation, high-resolution editing, generating consistent 3D views, and synthesizing videos with seamless transitions. All code and checkpoints of Lumina-T2X are released at https://github.com/Alpha-VLLM/Lumina-T2X to further foster creativity, transparency, and diversity in the generative AI community. | Generative Models, Text-to-Image Generation, Diffusion Models, Flow Matching | We propose Lumina-T2X, leveraging Flow-based Large Diffusion Transformer to transform noise into various resolution images with various advanced applications.. | 4,783 | null |
A Periodic Bayesian Flow for Material Generation | https://openreview.net/forum?id=Lz0XW99tE0 | [
"Hanlin Wu",
"Yuxuan Song",
"Jingjing Gong",
"Ziyao Cao",
"Yawen Ouyang",
"Jianbing Zhang",
"Hao Zhou",
"Wei-Ying Ma",
"Jingjing Liu"
] | Spotlight | Generative modeling of crystal data distribution is an important yet challenging task due to the unique periodic physical symmetry of crystals. Diffusion-based methods have shown early promise in modeling crystal distribution. More recently, Bayesian Flow Networks were introduced to aggregate noisy latent variables, resulting in a variance-reduced parameter space that has been shown to be advantageous for modeling Euclidean data distributions with structural constraints (Song, et al.,2023). Inspired by this, we seek to unlock its potential for modeling variables located in non-Euclidean manifolds e.g. those within crystal structures, by overcoming challenging theoretical issues. We introduce CrysBFN, a novel crystal generation method by proposing a periodic Bayesian flow, which essentially differs from the original Gaussian-based BFN by exhibiting non-monotonic entropy dynamics. To successfully realize the concept of periodic Bayesian flow, CrysBFN integrates a new entropy conditioning mechanism and empirically demonstrates its significance compared to time-conditioning. Extensive experiments over both crystal ab initio generation and crystal structure prediction tasks demonstrate the superiority of CrysBFN, which consistently achieves new state-of-the-art on all benchmarks. Surprisingly, we found that CrysBFN enjoys a significant improvement in sampling efficiency, e.g., 200x speedup (10 v.s. 2000 steps network forwards) compared with previous Diffusion-based methods on MP-20 dataset. | Crystal Generation, Bayesian Flow Networks, Crystal Structure Prediction | We propose a periodic Bayesian flow with a novel mechanism to generate material under entropy guidance, achieving consistent better performance and significant higher sampling efficiency. | 4,764 | 2502.02016 |
DiffPuter: Empowering Diffusion Models for Missing Data Imputation | https://openreview.net/forum?id=3fl1SENSYO | [
"Hengrui Zhang",
"Liancheng Fang",
"Qitian Wu",
"Philip S. Yu"
] | Spotlight | Generative models play an important role in missing data imputation in that they aim to learn the joint distribution of full data. However, applying advanced deep generative models (such as Diffusion models) to missing data imputation is challenging due to 1) the inherent incompleteness of the training data and 2) the difficulty in performing conditional inference from unconditional generative models. To deal with these challenges, this paper introduces DiffPuter, a tailored diffusion model combined with the Expectation-Maximization (EM) algorithm for missing data imputation. DiffPuter iteratively trains a diffusion model to learn the joint distribution of missing and observed data and performs an accurate conditional sampling to update the missing values using a tailored reversed sampling strategy. Our theoretical analysis shows that DiffPuter's training step corresponds to the maximum likelihood estimation of data density (M-step), and its sampling step represents the Expected A Posteriori estimation of missing values (E-step). Extensive experiments across ten diverse datasets and comparisons with 17 different imputation methods demonstrate DiffPuter's superior performance. Notably, DiffPuter achieves an average improvement of 8.10\% in MAE and 5.64\% in RMSE compared to the most competitive existing method. | Diffusion models, missing data imputation | This paper combines EM algorithm and a Diffusion model for missing data imputation | 4,746 | null |
Towards Marginal Fairness Sliced Wasserstein Barycenter | https://openreview.net/forum?id=NQqJPPCesd | [
"Khai Nguyen",
"Hai Nguyen",
"Nhat Ho"
] | Spotlight | The Sliced Wasserstein barycenter (SWB) is a widely acknowledged method for efficiently generalizing the averaging operation within probability measure spaces. However, achieving marginal fairness SWB, ensuring approximately equal distances from the barycenter to marginals, remains unexplored. The uniform weighted SWB is not necessarily the optimal choice to obtain the desired marginal fairness barycenter due to the heterogeneous structure of marginals and the non-optimality of the optimization. As the first attempt to tackle the problem, we define the marginal fairness sliced Wasserstein barycenter (MFSWB) as a constrained SWB problem. Due to the computational disadvantages of the formal definition, we propose two hyperparameter-free and computationally tractable surrogate MFSWB problems that implicitly minimize the distances to marginals and encourage marginal fairness at the same time. To further improve the efficiency, we perform slicing distribution selection and obtain the third surrogate definition by introducing a new slicing distribution that focuses more on marginally unfair projecting directions. We discuss the relationship of the three proposed problems and their relationship to sliced multi-marginal Wasserstein distance. Finally, we conduct experiments on finding 3D point-clouds averaging, color harmonization, and training of sliced Wasserstein autoencoder with class-fairness representation to show the favorable performance of the proposed surrogate MFSWB problems. | Sliced Wasserstein Barycenter, Optimal Transport, Sliced Wasserstein, Averaging Measures. | We discuss the notion of marginal fairness sliced Wasserstein barycenter which is a special case of sliced Wasserstein barycenter where the distances to marginals are approximately equal. | 4,722 | 2405.07482 |
Continuous Exposure Learning for Low-light Image Enhancement using Neural ODEs | https://openreview.net/forum?id=Mn2qgIcIPS | [
"Donggoo Jung",
"Daehyun Kim",
"Tae Hyun Kim"
] | Spotlight | Low-light image enhancement poses a significant challenge due to the limited information captured by image sensors in low-light environments.
Despite recent improvements in deep learning models, the lack of paired training datasets remains a significant obstacle.
Therefore, unsupervised methods have emerged as a promising solution.
In this work, we focus on the strength of curve-adjustment-based approaches to tackle unsupervised methods.
The majority of existing unsupervised curve-adjustment approaches iteratively estimate higher order curve parameters to enhance the exposure of images while efficiently preserving the details of the images.
However, the convergence of the enhancement procedure cannot be guaranteed, leading to sensitivity to the number of iterations and limited performance.
To address this problem, we consider the iterative curve-adjustment update process as a dynamic system and formulate it as a Neural Ordinary Differential Equations (NODE) for the first time, and this allows us to learn a continuous dynamics of the latent image.
The strategy of utilizing NODE to leverage continuous dynamics in iterative methods enhances unsupervised learning and aids in achieving better convergence compared to discrete-space approaches. Consequently, we achieve state-of-the-art performance in unsupervised low-light image enhancement across various benchmark datasets. | NeuralODE, Low-light Enhancement | This is a low-light image enhancement method using NeuralODE. | 4,698 | null |
Learning to Solve Differential Equation Constrained Optimization Problems | https://openreview.net/forum?id=VeMC6Bn0ZB | [
"Vincenzo Di Vito Francesco",
"Mostafa Mohammadian",
"Kyri Baker",
"Ferdinando Fioretto"
] | Spotlight | Differential equations (DE) constrained optimization plays a critical role in numerous scientific and engineering fields, including energy systems, aerospace engineering, ecology, and finance, where optimal configurations or control strategies must be determined for systems governed by ordinary or stochastic differential equations. Despite its significance, the computational challenges associated with these problems have limited their practical use. To address these limitations, this paper introduces a learning-based approach to DE-constrained optimization that combines techniques from proxy optimization \citep{kotary2021end} and neural differential equations \citep{chen2019neural}. The proposed approach uses a dual-network architecture, with one approximating the control strategies, focusing on steady-state constraints, and another solving the associated DEs.
This combination enables the approximation of optimal strategies while accounting for dynamic constraints in near real-time.
Experiments across problems in energy optimization and finance modeling show that this method provides full compliance with dynamic constraints and it produces results up to 25 times more precise than other methods which do not explicitly model the system's dynamic equations. | Learning-based optimization proxy, differential equations constrained optimization, neural differential equations, system dynamics | null | 4,668 | 2410.01786 |
Fast Uncovering of Protein Sequence Diversity from Structure | https://openreview.net/forum?id=1iuaxjssVp | [
"luca alessandro silva",
"Barthelemy Meynard-Piganeau",
"Carlo Lucibello",
"Christoph Feinauer"
] | Spotlight | We present InvMSAFold, an inverse folding method for generating protein sequences optimized for diversity and speed. For a given structure, InvMSAFold generates the parameters of a pairwise probability distribution over the space of sequences, capturing the amino acid covariances observed in Multiple Sequence Alignments (MSA) of homologous proteins. This allows for the efficient generation of highly diverse protein sequences while preserving structural and functional integrity.
We demonstrate that this increased diversity in sampled sequences translates into greater variability in biochemical properties, highlighting the exciting potential of our method for applications such as protein design. The orders of magnitude improvement in sampling speed compared to existing methods unlocks new possibilities for high-throughput in virtual screening. | Protein design, inverse folding, generative modelling, transfer learning | null | 4,663 | null |
Boosting Ray Search Procedure of Hard-label Attacks with Transfer-based Priors | https://openreview.net/forum?id=tIBAOcAvn4 | [
"Chen Ma",
"Xinjie Xu",
"Shuyu Cheng",
"Qi Xuan"
] | Spotlight | One of the most practical and challenging types of black-box adversarial attacks is the hard-label attack, where only top-1 predicted labels are available. One effective approach is to search for the optimal ray direction from the benign image that minimizes the $\ell_p$ norm distance to the adversarial region. The unique advantage of this approach is that it transforms the hard-label attack into a continuous optimization problem. The objective function value is the ray's radius and can be obtained through a binary search with high query cost. Existing methods use a "sign trick" in gradient estimation to reduce queries. In this paper, we theoretically analyze the quality of this gradient estimation, proposing a novel prior-guided approach to improve ray search efficiency, based on theoretical and experimental analysis. Specifically, we utilize the transfer-based priors from surrogate models, and our gradient estimators appropriately integrate them by approximating the projection of the true gradient onto the subspace spanned by these priors and some random directions, in a query-efficient way. We theoretically derive the expected cosine similarity between the obtained gradient estimators and the true gradient, and demonstrate the improvement brought by using priors. Extensive experiments on the ImageNet and CIFAR-10 datasets show that our approach significantly outperforms 11 state-of-the-art methods in terms of query efficiency. | adversarial attack, hard-label attack, decision-based attack, black-box attack, gradient estimation, surrogate model, transfer-based priors | We propose a novel prior-guided hard-label attack approach by using subspace projection approximation. | 4,515 | null |
3DIS: Depth-Driven Decoupled Image Synthesis for Universal Multi-Instance Generation | https://openreview.net/forum?id=MagmwodCAB | [
"dewei Zhou",
"Ji Xie",
"Zongxin Yang",
"Yi Yang"
] | Spotlight | The increasing demand for controllable outputs in text-to-image generation has spurred advancements in multi-instance generation (MIG), allowing users to define both instance layouts and attributes. However, unlike image-conditional generation methods such as ControlNet, MIG techniques have not been widely adopted in state-of-the-art models like SD2 and SDXL, primarily due to the challenge of building robust renderers that simultaneously handle instance positioning and attribute rendering. In this paper, we introduce Depth-Driven Decoupled Image Synthesis (3DIS), a novel framework that decouples the MIG process into two stages: (i) generating a coarse scene depth map for accurate instance positioning and scene composition, and (ii) rendering fine-grained attributes using pre-trained ControlNet on any foundational model, without additional training. Our 3DIS framework integrates a custom adapter into LDM3D for precise depth-based layouts and employs a finetuning-free method for enhanced instance-level attribute rendering. Extensive experiments on COCO-Position and COCO-MIG benchmarks demonstrate that 3DIS significantly outperforms existing methods in both layout precision and attribute rendering. Notably, 3DIS offers seamless compatibility with diverse foundational models, providing a robust, adaptable solution for advanced multi-instance generation. The code is available at: https://github.com/limuloo/3DIS. | Image Generation; Diffusion Models | null | 4,481 | null |
Strong Model Collapse | https://openreview.net/forum?id=et5l9qPUhm | [
"Elvis Dohmatob",
"Yunzhen Feng",
"Arjun Subramonian",
"Julia Kempe"
] | Spotlight | Within the scaling laws paradigm, which underpins the training of large neural networks like ChatGPT and Llama, we consider a supervised regression setting and establish a strong form of the model collapse phenomenon, a critical performance degradation due to synthetic data in the training corpus. Our results show that even the smallest fraction of synthetic data (e.g., as little as 1 per 1000) can still lead to model collapse: larger and larger training sets do not enhance performance. We further investigate whether increasing model size, an approach aligned with current trends in training large language models, exacerbates or mitigates model collapse. In a simplified regime where neural networks are approximated via random projections of tunable size, we both theoretically and empirically show that larger models can amplify model collapse. Interestingly, our theory also indicates that, beyond the interpolation threshold (which can be extremely high for very large datasets), larger models may mitigate the collapse, although they do not entirely prevent it. Our theoretical findings are empirically verified through experiments on language models and neural networks for images. | Model Collapse, Regression, High dimensional asymptotics, Synthetic Data, Scaling Laws | We provide an exact characterization of model collapse for mixing original and AI-generated data in the regression setting; even a small (but constant) fraction of synthetic data is detrimental asymptotically. We also study the impact of model size. | 4,448 | 2410.04840 |
DRoP: Distributionally Robust Data Pruning | https://openreview.net/forum?id=fxv0FfmDAg | [
"Artem M Vysogorets",
"Kartik Ahuja",
"Julia Kempe"
] | Spotlight | In the era of exceptionally data-hungry models, careful selection of the training data is essential to mitigate the extensive costs of deep learning. Data pruning offers a solution by removing redundant or uninformative samples from the dataset, which yields faster convergence and improved neural scaling laws. However, little is known about its impact on classification bias of the trained models. We conduct the first systematic study of this effect and reveal that existing data pruning algorithms can produce highly biased classifiers. We present theoretical analysis of the classification risk in a mixture of Gaussians to argue that choosing appropriate class pruning ratios, coupled with random pruning within classes has potential to improve worst-class performance. We thus propose DRoP, a distributionally robust approach to pruning and empirically demonstrate its performance on standard computer vision benchmarks. In sharp contrast to existing algorithms, our proposed method continues improving distributional robustness at a tolerable drop of average performance as we prune more from the datasets. | Data Pruning, Classification Bias, Robustness | We find that existing data pruning algorithms exacerbate classification bias and propose a simple robustness-aware random pruning protocol to address this deficiency. | 4,435 | 2404.05579 |
Co3Gesture: Towards Coherent Concurrent Co-speech 3D Gesture Generation with Interactive Diffusion | https://openreview.net/forum?id=VaowElpVzd | [
"Xingqun Qi",
"Yatian Wang",
"Hengyuan Zhang",
"Jiahao Pan",
"Wei Xue",
"Shanghang Zhang",
"Wenhan Luo",
"Qifeng Liu",
"Yike Guo"
] | Spotlight | Generating gestures from human speech has gained tremendous progress in animating virtual avatars. While the existing methods enable synthesizing gestures cooperated by people self-talking, they overlook the practicality of concurrent gesture modeling with two-person interactive conversations. Moreover, the lack of high-quality datasets with concurrent co-speech gestures also limits handling this issue. To fulfill this goal, we first construct a large-scale concurrent co-speech gesture dataset that contains more than 7M frames for diverse two-person interactive posture sequences, dubbed $\textbf{GES-Inter}$. Moreover, we propose Co$^{\mathbf{3}}$Gesture, a novel framework that enables concurrent coherent co-speech gesture synthesis including two-person interactive movements. Our framework is built upon two cooperative generation branches conditioned on decomposed speaker audio. Specifically, to enhance the coordination of human postures w.r.t corresponding speaker audios while interacting with the conversational partner, we present a Temporal-Interaction Module ($\textbf{TIM}$). TIM can effectively model the temporal association representation between two speakers' gesture sequences as interaction guidance and fuse it into the concurrent gesture generation. Then, we devise a mutual attention mechanism to further boost learning dependencies of interacted concurrent motions, thereby enabling us to generate vivid and coherent gestures. Extensive experiments demonstrate that our method outperforms the state-of-the-art models on our newly collected GES-Inter dataset. | 3D co-speech gesture generation, human motion modeling | We introduce the new task of concurrent co-speech gesture generation cooperating with one newly collected large-scale dataset named GES-Inter. | 4,425 | null |
Following the Human Thread in Social Navigation | https://openreview.net/forum?id=M8OGl34Pmg | [
"Luca Scofano",
"Alessio Sampieri",
"Tommaso Campari",
"Valentino Sacco",
"Indro Spinelli",
"Lamberto Ballan",
"Fabio Galasso"
] | Spotlight | The success of collaboration between humans and robots in shared environments relies on the robot's real-time adaptation to human motion. Specifically, in Social Navigation, the agent should be close enough to assist but ready to back up to let the human move freely, avoiding collisions. Human trajectories emerge as crucial cues in Social Navigation, but they are partially observable from the robot's egocentric view and computationally complex to process.
We present the first Social Dynamics Adaptation model (SDA) based on the robot's state-action history to infer the social dynamics. We propose a two-stage Reinforcement Learning framework: the first learns to encode the human trajectories into social dynamics and learns a motion policy conditioned on this encoded information, the current status, and the previous action. Here, the trajectories are fully visible, i.e., assumed as privileged information. In the second stage, the trained policy operates without direct access to trajectories. Instead, the model infers the social dynamics solely from the history of previous actions and statuses in real-time.
Tested on the novel Habitat 3.0 platform, SDA sets a novel state-of-the-art (SotA) performance in finding and following humans.
The code can be found at https://github.com/L-Scofano/SDA. | Embodied AI, Social Navigation, Human Trajectories | null | 4,339 | 2404.11327 |
Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL | https://openreview.net/forum?id=8oCrlOaYcc | [
"Ghada Sokar",
"Johan Samir Obando Ceron",
"Aaron Courville",
"Hugo Larochelle",
"Pablo Samuel Castro"
] | Spotlight | The use of deep neural networks in reinforcement learning (RL) often suffers from performance degradation as model size increases. While soft mixtures of experts (SoftMoEs) have recently shown promise in mitigating this issue for online RL, the reasons behind their effectiveness remain largely unknown. In this work we provide an in-depth analysis identifying the key factors driving this performance gain. We discover the surprising result that tokenizing the encoder output, rather than the use of multiple experts, is what is behind the efficacy of SoftMoEs. Indeed, we demonstrate that even with an appropriately scaled single expert, we are able to maintain the performance gains, largely thanks to tokenization. | Reinforcement learning, Deep reinforcement learning, Mixture of experts | We demonstrate that one of the main reasons for the efficacy when using mixtures of experts comes from the choice of tokenization, and we demonstrate that even with a single expert, tokenization is enough to yield the previously observed benefits. | 4,327 | null |
Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions | https://openreview.net/forum?id=lzdFImKK8w | [
"Xiaoran Jiao",
"Weian Mao",
"Wengong Jin",
"Peiyuan Yang",
"Hao Chen",
"Chunhua Shen"
] | Spotlight | Predicting the change in binding free energy ($\Delta \Delta G$) is crucial for understanding and modulating protein-protein interactions, which are critical in drug design.
Due to the scarcity of experimental $\Delta\Delta G$ data,
existing methods focus on pre-training,
while neglecting the importance of alignment.
In this work, we propose Boltzmann Alignment technique to transfer knowledge from pre-trained inverse folding models to prediction of $\Delta\Delta G$.
We begin by analyzing the thermodynamic definition of $\Delta\Delta G$ and introducing the Boltzmann distribution to connect energy to the protein conformational distribution.
However, the protein conformational distribution is intractable. Therefore, we employ Bayes’ theorem to circumvent direct estimation and instead utilize the log-likelihood provided by protein inverse folding models for the estimation of $\Delta\Delta G$.
Compared to previous methods based on inverse folding, our method explicitly accounts for the unbound state of the protein complex in the $\Delta \Delta G$ thermodynamic cycle, introducing a physical inductive bias and achieving supervised and unsupervised state-of-the-art (SoTA) performance.
Experimental results on SKEMPI v2 indicate that our method achieves Spearman coefficients of 0.3201 (unsupervised) and 0.5134 (supervised) on SKEMPI v2, significantly surpassing the previously reported
%SoTA values
SoTA results
of 0.2632 and 0.4324, respectively.
Furthermore, we demonstrate the capability of our method in binding
energy prediction, protein-protein docking, and antibody optimization tasks.
Code is available at [https://github.com/aim-uofa/BA-DDG](https://github.com/aim-uofa/BA-DDG) | Mutational Effects; Protein-Protein Interactions | null | 4,282 | 2410.09543 |
Improving Unsupervised Constituency Parsing via Maximizing Semantic Information | https://openreview.net/forum?id=qyU5s4fzLg | [
"Junjie Chen",
"Xiangheng He",
"Yusuke Miyao",
"Danushka Bollegala"
] | Spotlight | Unsupervised constituency parsers organize phrases within a sentence into a tree-shaped syntactic constituent structure that reflects the organization of sentence semantics.
However, the traditional objective of maximizing sentence log-likelihood (LL) does not explicitly account for the close relationship between the constituent structure and the semantics, resulting in a weak correlation between LL values and parsing accuracy.
In this paper, we introduce a novel objective that trains parsers by maximizing SemInfo, the semantic information encoded in constituent structures.
We introduce a bag-of-substrings model to represent the semantics and estimate the SemInfo value using the probability-weighted information metric.
We apply the SemInfo maximization objective to training Probabilistic Context-Free Grammar (PCFG) parsers and develop a Tree Conditional Random Field (TreeCRF)-based model to facilitate the training.
Experiments show that SemInfo correlates more strongly with parsing accuracy than LL, establishing SemInfo as a better unsupervised parsing objective.
As a result, our algorithm significantly improves parsing accuracy by an average of 7.85 sentence-F1 scores across five PCFG variants and in four languages, achieving state-of-the-art level results in three of the four languages. | unsupervised constituency parsing, information theory, semantic information | null | 4,272 | 2410.02558 |
Student-Informed Teacher Training | https://openreview.net/forum?id=Dzh0hQPpuf | [
"Nico Messikommer",
"Jiaxu Xing",
"Elie Aljalbout",
"Davide Scaramuzza"
] | Spotlight | Imitation learning with a privileged teacher has proven effective for learning complex control behaviors from high-dimensional inputs, such as images. In this framework, a teacher is trained with privileged task information, while a student tries to predict the actions of the teacher with more limited observations, e.g., in a robot navigation task, the teacher might have access to distances to nearby obstacles, while the student only receives visual observations of the scene. However, privileged imitation learning faces a key challenge: the student might be unable to imitate the teacher's behavior due to partial observability. This problem arises because the teacher is trained without considering if the student is capable of imitating the learned behavior. To address this teacher-student asymmetry, we propose a framework for joint training of the teacher and student policies, encouraging the teacher to learn behaviors that can be imitated by the student despite the latters' limited access to information and its partial observability. Based on the performance bound in imitation learning, we add (i) the approximated action difference between teacher and student as a penalty term to the reward function of the teacher, and (ii) a supervised teacher-student alignment step. We motivate our method with a maze navigation task and demonstrate its effectiveness on complex vision-based quadrotor flight and manipulation tasks. | Reinforcement Learning, Imitation Learning, Robotics | To address the teacher-student asymmetry in imitation learning, we propose a joint learning framework for both teacher and student, adapting the teacher to the capabilities of the student during training to enhance alignment. | 4,246 | 2412.09149 |
OS-ATLAS: Foundation Action Model for Generalist GUI Agents | https://openreview.net/forum?id=n9PDaFNi8t | [
"Zhiyong Wu",
"Zhenyu Wu",
"Fangzhi Xu",
"Yian Wang",
"Qiushi Sun",
"Chengyou Jia",
"Kanzhi Cheng",
"Zichen Ding",
"Liheng Chen",
"Paul Pu Liang",
"Yu Qiao"
] | Spotlight | Existing efforts in building GUI agents heavily rely on the availability of robust commercial Vision-Language Models (VLMs) such as GPT-4o and GeminiProVision. Practitioners are often reluctant to use open-source VLMs due to their significant performance lag compared to their closed-source counterparts, particularly in GUI grounding and Out-Of-Distribution (OOD) scenarios. To facilitate future research in this area, we developed OS-Atlas—a foundational GUI action model that excels at GUI grounding and OOD agentic tasks through innovations in both data and modeling.
We have invested significant engineering effort in developing an open-source toolkit for synthesizing GUI grounding data across multiple platforms, including Windows, Linux, MacOS, Android, and the web. Leveraging this toolkit, we are releasing the largest open-source cross-platform GUI grounding corpus to date, which contains over 13 million GUI elements. This dataset, combined with innovations in model training, provides a solid foundation for OS-Atlas to understand GUI screenshots and generalize to unseen interfaces.
Through extensive evaluation across six benchmarks spanning three different platforms (mobile, desktop, and web), OS-Atlas demonstrates significant performance improvements over previous state-of-the-art models. Our evaluation also uncovers valuable insights into continuously improving and scaling the agentic capabilities of open-source VLMs. | GUI agent, language agent, GUI grounding, executable language grounding | Through innovations in both data and modeling, we are releasing the first foundational action model specifically designed for GUI agents. | 4,239 | null |
Sample then Identify: A General Framework for Risk Control and Assessment in Multimodal Large Language Models | https://openreview.net/forum?id=9WYMDgxDac | [
"Qingni Wang",
"Tiantian Geng",
"Zhiyuan Wang",
"Teng Wang",
"Bo Fu",
"Feng Zheng"
] | Spotlight | Multimodal Large Language Models (MLLMs) exhibit promising advancements across various tasks, yet they still encounter significant trustworthiness issues. Prior studies apply Split Conformal Prediction (SCP) in language modeling to construct prediction sets with statistical guarantees. However, these methods typically rely on internal model logits or are restricted to multiple-choice settings, which hampers their generalizability and adaptability in dynamic, open-ended environments. In this paper, we introduce *TRON*, a **t**wo-step framework for **r**isk c**o**ntrol and assessme**n**t, applicable to any MLLM that supports sampling in both open-ended and closed-ended scenarios. *TRON* comprises two main components: (1) a novel conformal score to **sample** response sets of minimum size, and (2) a nonconformity score to **identify** high-quality responses based on self-consistency theory, controlling the error rates by two specific risk levels. Furthermore, we investigate semantic redundancy in prediction sets within open-ended contexts for the first time, leading to a promising evaluation metric for MLLMs based on average set size. Our comprehensive experiments across four Video Question-Answering (VideoQA) datasets utilizing eight MLLMs show that *TRON* achieves desired error rates bounded by two user-specified risk levels. Additionally, deduplicated prediction sets maintain adaptiveness while being more efficient and stable for risk assessment under different risk levels. | generative models, calibration/uncertainty, inference methods | We propose a general framework for risk control and assessment, which creates rigorous prediction sets with statistical guarantees calibrated by two user-specified risk levels, applicable to any MLLMs supporting sampling in open-ended settings. | 4,201 | 2410.08174 |
Exploring the Camera Bias of Person Re-identification | https://openreview.net/forum?id=SgymXhOEA5 | [
"Myungseo Song",
"Jin-Woo Park",
"Jong-Seok Lee"
] | Spotlight | We empirically investigate the camera bias of person re-identification (ReID) models. Previously, camera-aware methods have been proposed to address this issue, but they are largely confined to training domains of the models. We measure the camera bias of ReID models on unseen domains and reveal that camera bias becomes more pronounced under data distribution shifts. As a debiasing method for unseen domain data, we revisit feature normalization on embedding vectors. While the normalization has been used as a straightforward solution, its underlying causes and broader applicability remain unexplored. We analyze why this simple method is effective at reducing bias and show that it can be applied to detailed bias factors such as low-level image properties and body angle. Furthermore, we validate its generalizability across various models and benchmarks, highlighting its potential as a simple yet effective test-time postprocessing method for ReID. In addition, we explore the inherent risk of camera bias in unsupervised learning of ReID models. The unsupervised models remain highly biased towards camera labels even for seen domain data, indicating substantial room for improvement. Based on observations of the negative impact of camera-biased pseudo labels on training, we suggest simple training strategies to mitigate the bias. By applying these strategies to existing unsupervised learning algorithms, we show that significant performance improvements can be achieved with minor modifications. | Person re-identification, Camera bias, Debiasing, Unsupervised learning | We investigate the camera bias problem of ReID models, including debiasing effects of feature normalization and risk of camera bias in unsupervised learning. | 4,178 | 2502.10195 |
Preference Optimization for Reasoning with Pseudo Feedback | https://openreview.net/forum?id=jkUp3lybXf | [
"Fangkai Jiao",
"Geyang Guo",
"Xingxing Zhang",
"Nancy F. Chen",
"Shafiq Joty",
"Furu Wei"
] | Spotlight | Preference optimization techniques, such as Direct Preference Optimization (DPO), are frequently employed to enhance the reasoning capabilities of large language models (LLMs) in domains like mathematical reasoning and coding, typically following supervised fine-tuning. These methods rely on high-quality labels for reasoning tasks to generate preference pairs; however, the availability of reasoning datasets with human-verified labels is limited.
In this study, we introduce a novel approach to generate pseudo feedback for reasoning tasks by framing the labeling of solutions to reason problems as an evaluation against associated \emph{test cases}.
We explore two forms of pseudo feedback based on test cases: one generated by frontier LLMs and the other by extending self-consistency to multi-test-case.
We conduct experiments on both mathematical reasoning and coding tasks using pseudo feedback for preference optimization, and observe improvements across both tasks. Specifically, using Mathstral-7B as our base model, we improve MATH results from 58.3 to 68.6, surpassing both NuminaMath-72B and GPT-4-Turbo-1106-preview. In GSM8K and College Math, our scores increase from 85.6 to 90.3 and from 34.3 to 42.3, respectively. Building on Deepseek-coder-7B-v1.5, we achieve a score of 24.3 on LiveCodeBench (from 21.1), surpassing Claude-3-Haiku. | Large Language Model, Code Generation, Natural Language Reasoning, Reinforcement Learning | We develop a framework to continuously improve LLMs for reasoning and code generation through self-consistency-based pseudo feedback. | 4,129 | 2411.16345 |
Regularization by Texts for Latent Diffusion Inverse Solvers | https://openreview.net/forum?id=TtUh0TOlGX | [
"Jeongsol Kim",
"Geon Yeong Park",
"Hyungjin Chung",
"Jong Chul Ye"
] | Spotlight | The recent development of diffusion models has led to significant progress in solving inverse problems by leveraging these models as powerful generative priors. However, challenges persist due to the ill-posed nature of such problems, often arising from ambiguities in measurements or intrinsic system symmetries. To address this, we introduce a novel latent diffusion inverse solver, regularization by text (TReg), inspired by the human ability to resolve visual ambiguities through perceptual biases. TReg integrates textual descriptions of preconceptions about the solution during reverse diffusion sampling, dynamically reinforcing these descriptions through null-text optimization, which we refer to as adaptive negation. Our comprehensive experimental results demonstrate that TReg effectively mitigates ambiguity in inverse problems, improving both accuracy and efficiency. | Inverse problem, Text regularization, Diffusion model | We propose text regularization for inverse problem solving. | 4,127 | 2311.15658 |
Multi-modal Agent Tuning: Building a VLM-Driven Agent for Efficient Tool Usage | https://openreview.net/forum?id=0bmGL4q7vJ | [
"Zhi Gao",
"Bofei Zhang",
"Pengxiang Li",
"Xiaojian Ma",
"Tao Yuan",
"Yue Fan",
"Yuwei Wu",
"Yunde Jia",
"Song-Chun Zhu",
"Qing Li"
] | Spotlight | The advancement of large language models (LLMs) prompts the development of multi-modal agents, which are used as a controller to call external tools, providing a feasible way to solve practical tasks. In this paper, we propose a multi-modal agent tuning method that automatically generates multi-modal tool-usage data and tunes a vision-language model (VLM) as the controller for powerful tool-usage reasoning. To preserve the data quality, we prompt the GPT-4o mini model to generate queries, files, and trajectories, followed by query-file and trajectory verifiers. Based on the data synthesis pipeline, we collect the MM-Traj dataset that contains 20K tasks with trajectories of tool usage. Then, we develop the T3-Agent via Trajectory Tuning on VLMs for Tool usage using MM-Traj. Evaluations on the GTA and GAIA benchmarks show that the T3-Agent consistently achieves improvements on two popular VLMs: MiniCPM-V-8.5B and Qwen2-VL-7B, which outperforms untrained VLMs by 20%, showing the effectiveness of the proposed data synthesis pipeline, leading to high-quality data for tool-usage capabilities. | Multimodal Agents, Vision-language Model, Tool usage | null | 4,107 | 2412.15606 |
Monitoring Latent World States in Language Models with Propositional Probes | https://openreview.net/forum?id=0yvZm2AjUr | [
"Jiahai Feng",
"Stuart Russell",
"Jacob Steinhardt"
] | Spotlight | Language models (LMs) are susceptible to bias, sycophancy, backdoors, and other tendencies that lead to unfaithful responses to the input context. Interpreting internal states of LMs could help monitor and correct unfaithful behavior. We hypothesize that LMs faithfully represent their input contexts in a latent world model, and we seek to extract these latent world states as logical propositions. For example, given the input context ``Greg is a nurse. Laura is a physicist.'', we aim to decode the propositions WorksAs(Greg, nurse) and WorksAs(Laura, physicist) from the model's internal activations. To do so we introduce _propositional probes_, which compositionally extract lexical concepts from token activations and bind them into propositions. Key to this is identifying a _binding subspace_ in which bound tokens have high similarity (Greg $\leftrightarrow$ nurse) but unbound ones do not (Greg $\not\leftrightarrow$ physicist). Despite only being trained on linguistically simple English templates, we find that propositional probes generalize to inputs written as short stories and translated to Spanish. Moreover, in three settings where LMs respond unfaithfully to the input context---prompt injections, backdoor attacks, and gender bias--- the decoded propositions remain faithful. This suggests that LMs often encode a faithful world model but decode it unfaithfully, which motivates the search for better interpretability tools for monitoring LMs. | Interpretability, Language models, AI Safety | We develop propositional probes, which extract logical propositions describing a language model's internal world state | 4,104 | 2406.19501 |
TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models | https://openreview.net/forum?id=cqsw28DuMW | [
"Makoto Shing",
"Kou Misaki",
"Han Bao",
"Sho Yokoi",
"Takuya Akiba"
] | Spotlight | Causal language models have demonstrated remarkable capabilities, but their size poses significant challenges for deployment in resource-constrained environments. Knowledge distillation, a widely-used technique for transferring knowledge from a large teacher model to a small student model, presents a promising approach for model compression.
A significant remaining issue lies in the major differences between teacher and student models, namely the substantial capacity gap, mode averaging, and mode collapse, which pose barriers during distillation.
To address these issues, we introduce $\textit{Temporally Adaptive Interpolated Distillation (TAID)}$, a novel knowledge distillation approach that dynamically interpolates student and teacher distributions through an adaptive intermediate distribution, gradually shifting from the student's initial distribution towards the teacher's distribution. We provide a theoretical analysis demonstrating TAID's ability to prevent mode collapse and empirically show its effectiveness in addressing the capacity gap while balancing mode averaging and mode collapse.
Our comprehensive experiments demonstrate TAID's superior performance across various model sizes and architectures in both instruction tuning and pre-training scenarios. Furthermore, we showcase TAID's practical impact by developing two state-of-the-art compact foundation models: $\texttt{TAID-LLM-1.5B}$ for language tasks and $\texttt{TAID-VLM-2B}$ for vision-language tasks.
These results demonstrate TAID's effectiveness in creating high-performing and efficient models, advancing the development of more accessible AI technologies. | Lanauge Models, Knowledge Distillation | We propose TAID, a novel knowledge distillation method for language models that uses a time-dependent intermediate distribution to dynamically bridge student-teacher gaps, addressing common challenges in distilling large language models. | 4,087 | 2501.16937 |
Learning Transformer-based World Models with Contrastive Predictive Coding | https://openreview.net/forum?id=YK9G4Htdew | [
"Maxime Burchi",
"Radu Timofte"
] | Spotlight | The DreamerV3 algorithm recently obtained remarkable performance across diverse environment domains by learning an accurate world model based on Recurrent Neural Networks (RNNs). Following the success of model-based reinforcement learning algorithms and the rapid adoption of the Transformer architecture for its superior training efficiency and favorable scaling properties, recent works such as STORM have proposed replacing RNN-based world models with Transformer-based world models using masked self-attention. However, despite the improved training efficiency of these methods, their impact on performance remains limited compared to the Dreamer algorithm, struggling to learn competitive Transformer-based world models. In this work, we show that the next state prediction objective adopted in previous approaches is insufficient to fully exploit the representation capabilities of Transformers. We propose to extend world model predictions to longer time horizons by introducing TWISTER (Transformer-based World model wIth contraSTivE Representations), a world model using action-conditioned Contrastive Predictive Coding to learn high-level temporal feature representations and improve the agent performance. TWISTER achieves a human-normalized mean score of 162% on the Atari 100k benchmark, setting a new record among state-of-the-art methods that do not employ look-ahead search. We release our code at https://github.com/burchim/TWISTER. | model-based reinforcement learning, transformer network, contrastive predictive coding | We introduce TWISTER, a Transformer model-based reinforcement learning algorithm using action-conditioned Contrastive Predictive Coding to learn high-level feature representations and improve the agent performance. | 3,995 | 2503.04416 |
Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentation | https://openreview.net/forum?id=jXvwJ51vcK | [
"Zhaochong An",
"Guolei Sun",
"Yun Liu",
"Runjia Li",
"Min Wu",
"Ming-Ming Cheng",
"Ender Konukoglu",
"Serge Belongie"
] | Spotlight | Few-shot 3D point cloud segmentation (FS-PCS) aims at generalizing models to segment novel categories with minimal annotated support samples. While existing FS-PCS methods have shown promise, they primarily focus on unimodal point cloud inputs, overlooking the potential benefits of leveraging multimodal information. In this paper, we address this gap by introducing a multimodal FS-PCS setup, utilizing textual labels and the potentially available 2D image modality. Under this easy-to-achieve setup, we present the MultiModal Few-Shot SegNet (MM-FSS), a model effectively harnessing complementary information from multiple modalities. MM-FSS employs a shared backbone with two heads to extract intermodal and unimodal visual features, and a pretrained text encoder to generate text embeddings. To fully exploit the multimodal information, we propose a Multimodal Correlation Fusion (MCF) module to generate multimodal correlations, and a Multimodal Semantic Fusion (MSF) module to refine the correlations using text-aware semantic guidance. Additionally, we propose a simple yet effective Test-time Adaptive Cross-modal Calibration (TACC) technique to mitigate training bias, further improving generalization. Experimental results on S3DIS and ScanNet datasets demonstrate significant performance improvements achieved by our method. The efficacy of our approach indicates the benefits of leveraging commonly-ignored free modalities for FS-PCS, providing valuable insights for future research. The code is available at github.com/ZhaochongAn/Multimodality-3D-Few-Shot. | 3D segmentation, 3D point cloud, few-shot segmentation, multimodality, few-shot point cloud semantic segmentation | null | 3,954 | 2410.22489 |
MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark | https://openreview.net/forum?id=TeVAZXr3yv | [
"S Sakshi",
"Utkarsh Tyagi",
"Sonal Kumar",
"Ashish Seth",
"Ramaneswaran Selvakumar",
"Oriol Nieto",
"Ramani Duraiswami",
"Sreyan Ghosh",
"Dinesh Manocha"
] | Spotlight | The ability to comprehend audio—which includes speech, non-speech sounds, and music—is crucial for AI agents to interact effectively with the world. We present MMAU, a novel benchmark designed to evaluate multimodal audio understanding models on tasks requiring expert-level knowledge and complex reasoning. MMAU comprises 10k carefully curated audio clips paired with human-annotated natural language questions and answers spanning speech, environmental sounds, and music. It includes information extraction and reasoning questions, requiring models to demonstrate 27 distinct skills across unique and challenging tasks. Unlike existing benchmarks, MMAU emphasizes advanced perception and reasoning with domain-specific knowledge, challenging models to tackle tasks akin to those faced by experts. We assess 18 open-source and proprietary (Large) Audio-Language Models, demonstrating the significant challenges
posed by MMAU. Notably, even the most advanced Gemini 2.0 Flash achieves only 59.93% accuracy, and the state-of-the-art open-source Qwen2-Audio achieves only 52.50%, highlighting considerable room for improvement. We believe MMAU will drive the audio and multimodal research community to develop more advanced audio understanding models capable of solving complex audio tasks. | Benchmark, Audio Language Models, Complex Reasoning | We introduce MMAU, a comprehensive benchmark evaluating multimodal audio understanding across speech, sound, and music, challenging models with 27 distinct skills requiring advanced perception, reasoning, and domain-specific knowledge. | 3,927 | 2410.19168 |
Higher-Order Graphon Neural Networks: Approximation and Cut Distance | https://openreview.net/forum?id=SjufxrSOYd | [
"Daniel Herbst",
"Stefanie Jegelka"
] | Spotlight | Graph limit models, like *graphons* for limits of dense graphs, have recently been used to study size transferability of graph neural networks (GNNs). While most literature focuses on message passing GNNs (MPNNs), in this work we attend to the more powerful *higher-order* GNNs. First, we extend the $k$-WL test for graphons (Böker, 2023) to the graphon-signal space and introduce *signal-weighted homomorphism densities* as a key tool. As an exemplary focus, we generalize *Invariant Graph Networks* (IGNs) to graphons, proposing *Invariant Graphon Networks* (IWNs) defined via a subset of the IGN basis corresponding to bounded linear operators. Even with this restricted basis, we show that IWNs of order $k$ are at least as powerful as the $k$-WL test, and we establish universal approximation results for graphon-signals in $L^p$ distances. This significantly extends the prior work of Cai & Wang (2022), showing that IWNs—a subset of their *IGN-small*—retain effectively the same expressivity as the full IGN basis in the limit. In contrast to their approach, our blueprint of IWNs also aligns better with the geometry of graphon space, for example facilitating comparability to MPNNs. We highlight that, while typical higher-order GNNs are discontinuous w.r.t.\ cut distance—which causes their lack of convergence and is inherently tied to the definition of $k$-WL—their transferability remains comparable to MPNNs. | Graph neural networks, invariant graph networks, universal approximation, graph limits, graphons, transferability, homomorphism densities, machine learning theory. | We extend higher-order GNNs to the graphon space and investigate their continuity, expressivity, and transferability. | 3,892 | 2503.14338 |
Towards General-Purpose Model-Free Reinforcement Learning | https://openreview.net/forum?id=R1hIXdST22 | [
"Scott Fujimoto",
"Pierluca D'Oro",
"Amy Zhang",
"Yuandong Tian",
"Michael Rabbat"
] | Spotlight | Reinforcement learning (RL) promises a framework for near-universal problem-solving. In practice however, RL algorithms are often tailored to specific benchmarks, relying on carefully tuned hyperparameters and algorithmic choices. Recently, powerful model-based RL methods have shown impressive general results across benchmarks but come at the cost of increased complexity and slow run times, limiting their broader applicability. In this paper, we attempt to find a unifying model-free deep RL algorithm that can address a diverse class of domains and problem settings. To achieve this, we leverage model-based representations that approximately linearize the value function, taking advantage of the denser task objectives used by model-based RL while avoiding the costs associated with planning or simulated trajectories. We evaluate our algorithm, MR.Q, on a variety of common RL benchmarks with a single set of hyperparameters and show a competitive performance against domain-specific and general baselines, providing a concrete step towards building general-purpose model-free deep RL algorithms. | Deep reinforcement learning, model-free, general-purpose | We introduce a model-free RL algorithm with a strong performance on a variety of popular benchmarks. | 3,889 | 2501.16142 |
Multi-Field Adaptive Retrieval | https://openreview.net/forum?id=3PDklqqqfN | [
"Millicent Li",
"Tongfei Chen",
"Benjamin Van Durme",
"Patrick Xia"
] | Spotlight | Document retrieval for tasks such as search and retrieval-augmented generation typically involves datasets that are _unstructured_: free-form text without explicit internal structure in each document. However, documents can have some structure, containing fields such as an article title, a message body, or an HTML header. To address this gap, we introduce Multi-Field Adaptive Retrieval (mFAR), a flexible framework that accommodates any number and any type of document indices on _semi-structured_ data. Our framework consists of two main steps: (1) the decomposition of an existing document into fields, each indexed independently through dense and lexical methods, and (2) learning a model which adaptively predicts the importance of a field by conditioning on the document query, allowing on-the-fly weighting of the most likely field(s). We find that our approach allows for the optimized use of dense versus lexical representations across field types, significantly improves in document ranking over a number of existing retrievers, and achieves state-of-the-art performance for multi-field structured data. | information retrieval, hybrid retrievers, semi-structured data | We introduce a framework for retrieval over structured documents that adaptively accommodates multiple scorers. | 3,882 | 2410.20056 |
Scaling FP8 training to trillion-token LLMs | https://openreview.net/forum?id=E1EHO0imOb | [
"Maxim Fishman",
"Brian Chmiel",
"Ron Banner",
"Daniel Soudry"
] | Spotlight | We train, for the first time, large language models using FP8 precision on datasets up to 2 trillion tokens --- a 20-fold increase over previous limits. Through these extended training runs, we uncover critical instabilities in FP8 training that were not observable in earlier works with shorter durations. We trace these instabilities to outlier amplification by the SwiGLU activation function. Interestingly, we show, both analytically and empirically, that this amplification happens only over prolonged training periods, and link it to a SwiGLU weight alignment process. To address this newly identified issue, we introduce Smooth-SwiGLU, a novel modification that ensures stable FP8 training without altering function behavior. We also demonstrate, for the first time, FP8 quantization of both Adam optimizer moments. Combining these innovations, we successfully train a 7B parameter model using FP8 precision on 256 Intel Gaudi2 accelerators, achieving on-par results with the BF16 baseline while delivering up to a $\sim$ 34 % throughput improvement. A reference implementation is supplied in https://github.com/Anonymous1252022/Megatron-DeepSpeed | quantization, fp8, llms, training, acceleration, compression | Training LLMs up to 2 trillion tokens, for the first time in FP8 precision | 3,789 | 2409.12517 |
Topograph: An Efficient Graph-Based Framework for Strictly Topology Preserving Image Segmentation | https://openreview.net/forum?id=Q0zmmNNePz | [
"Laurin Lux",
"Alexander H Berger",
"Alexander Weers",
"Nico Stucki",
"Daniel Rueckert",
"Ulrich Bauer",
"Johannes C. Paetzold"
] | Spotlight | Topological correctness plays a critical role in many image segmentation tasks, yet most networks are trained using pixel-wise loss functions, such as Dice, neglecting topological accuracy. Existing topology-aware methods often lack robust topological guarantees, are limited to specific use cases, or impose high computational costs.
In this work, we propose a novel, graph-based framework for topologically accurate image segmentation that is both computationally efficient and generally applicable. Our method constructs a component graph that fully encodes the topological information of both the prediction and ground truth, allowing us to efficiently identify topologically critical regions and aggregate a loss based on local neighborhood information. Furthermore, we introduce a strict topological metric capturing the homotopy equivalence between the union and intersection of prediction-label pairs. We formally prove the topological guarantees of our approach and empirically validate its effectiveness on binary and multi-class datasets, demonstrating state-of-the-art performance with up to fivefold faster loss computation compared to persistent homology methods. | Image Segmentation, Topology, Graph | A novel metric and loss function based on component graphs for topology preserving image segmentation. | 3,773 | 2411.03228 |
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