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stringclasses 763
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Learning to plan;Reinforcement Learning;Value Iteration;Navigation;Convnets
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
Value Propagation Networks
| null | null | 0 | 3 |
Workshop
|
4;2;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Automated Design;Gradient Descent
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
AUTOMATED DESIGN USING NEURAL NETWORKS AND GRADIENT DESCENT
| null | null | 0 | 4.333333 |
Workshop
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Musical audio;neural style transfer;Time-Frequency;Spectrogram
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
“Style” Transfer for Musical Audio Using Multiple Time-Frequency Representations
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
distributed representations;sentence embedding;representation learning;unsupervised learning;encoder-decoder;RNN
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Decoding Decoders: Finding Optimal Representation Spaces for Unsupervised Similarity Tasks
| null | null | 0 | 4.333333 |
Workshop
|
4;5;4
| null |
null |
Microsoft Research, Canada; Ecole Polytechnique, Canada; Montreal Institute for Learning Algorithms (MILA), Canada
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Dmitriy Serdyuk, Nan Rosemary Ke, Alessandro Sordoni, Adam Trischler, Christopher Pal, Yoshua Bengio
|
https://iclr.cc/virtual/2018/poster/86
|
generative rnns;long term dependencies;speech recognition;image captioning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Twin Networks: Matching the Future for Sequence Generation
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
Max-Planck Institute for Informatics, Saarland Informatics Campus, Saarbrücken, Germany
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Seong Joon Oh, Max Augustin, Mario Fritz, Bernt Schiele
|
https://iclr.cc/virtual/2018/poster/243
|
black box;security;privacy;attack;metamodel;adversarial example;reverse-engineering;machine learning
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.333333 |
5;7;7
| null | null |
Towards Reverse-Engineering Black-Box Neural Networks
|
https://goo.gl/MbYfsv
| null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null |
University of Siegen; Technical University of Munich
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Thomas Frerix, Thomas Möllenhoff, Michael Moeller, Daniel Cremers
|
https://iclr.cc/virtual/2018/poster/202
| null | null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Proximal Backpropagation
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
optimal control;reinforcement learning
| null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Towards Provable Control for Unknown Linear Dynamical Systems
| null | null | 0 | 3.333333 |
Workshop
|
3;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
structured attention;sentence matching
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
STRUCTURED ALIGNMENT NETWORKS
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Named Entities;Neural methods;Goal oriented dialog
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
3;4;6
| null | null |
A Neural Method for Goal-Oriented Dialog Systems to interact with Named Entities
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
DenseNets;Tensor Analysis;Convolutional Arithmetic Circuits
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
A Tensor Analysis on Dense Connectivity via Convolutional Arithmetic Circuits
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Generative Models;GANs
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Flexible Prior Distributions for Deep Generative Models
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural Language Processing;Machine Translation;Deep Learning;Data Augmentation
| null | 0 | null | null |
iclr
| -0.654654 | 0 | null |
main
| 4.666667 |
3;5;6
| null | null |
A cluster-to-cluster framework for neural machine translation
| null | null | 0 | 3 |
Withdraw
|
4;2;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
dialogue generation;dialogue acts;open domain conversation;supervised learning;reinforcement learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 6 |
4;7;7
| null | null |
Towards Interpretable Chit-chat: Open Domain Dialogue Generation with Dialogue Acts
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Learning Topics using Semantic Locality
| null | null | 0 | 4.333333 |
Withdraw
|
4;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
target propagation;biologically-plausible learning;benchmark;neuroscience
| null | 0 | null | null |
iclr
| 0.981981 | 0 | null |
main
| 6.333333 |
5;6;8
| null | null |
Assessing the scalability of biologically-motivated deep learning algorithms and architectures
| null | null | 0 | 4 |
Withdraw
|
3;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.333333 |
5;5;6
| null | null |
MACHINE VS MACHINE: MINIMAX-OPTIMAL DEFENSE AGAINST ADVERSARIAL EXAMPLES
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Natural Language Processing;Deep Learning;Reasoning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Finding ReMO (Related Memory Object): A Simple neural architecture for Text based Reasoning
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
University of California, Los Angeles; École Normale Supérieure de Lyon; École Polytechnique Fédérale de Lausanne
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Seyed Mohsen Moosavi Dezfooli, Alhussein Fawzi, Omar Fawzi, Pascal Frossard, Stefano Soatto
|
https://iclr.cc/virtual/2018/poster/286
|
Universal perturbations;robustness;curvature
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Robustness of Classifiers to Universal Perturbations: A Geometric Perspective
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null |
Department of Computer Science, Stanford University
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Aditi Raghunathan, Jacob Steinhardt, Percy Liang
|
https://iclr.cc/virtual/2018/poster/116
|
adversarial examples;certificate of robustness;convex relaxations
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 7 |
5;8;8
| null | null |
Certified Defenses against Adversarial Examples
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;regularization
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Achieving Strong Regularization for Deep Neural Networks
|
https://github.com/(anonymized)
| null | 0 | 4 |
Reject
|
5;5;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Dialog Systems;Language Generation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Placeholder
| null | null | 0 | 0 |
Withdraw
| null | null |
null |
Department of Statistics, Columbia University; Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
gonzalo mena, David Belanger, Scott Linderman, Jasper Snoek
|
https://iclr.cc/virtual/2018/poster/183
|
Permutation;Latent;Sinkhorn;Inference;Optimal Transport;Gumbel;Softmax;Sorting
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Learning Latent Permutations with Gumbel-Sinkhorn Networks
| null | null | 0 | 3.333333 |
Poster
|
2;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
generative adversarial networks;Wasserstein;GAN;generalization;theory
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Towards a Testable Notion of Generalization for Generative Adversarial Networks
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial Training;Privacy Protection;Random Subspace
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Censoring Representations with Multiple-Adversaries over Random Subspaces
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null |
University of Washington, Seattle
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Tianyi Zhou, Jeff Bilmes
|
https://iclr.cc/virtual/2018/poster/276
|
machine teaching;deep learning;minimax;curriculum learning;submodular;diversity
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Minimax Curriculum Learning: Machine Teaching with Desirable Difficulties and Scheduled Diversity
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null |
Paper under double-blind review
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Training Deep Models;Non-convex Optimization;Local and Global Equivalence;Local Openness
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
Learning Deep Models: Critical Points and Local Openness
| null | null | 0 | 4 |
Workshop
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
representation learning;disentanglement;regularization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Disentangled activations in deep networks
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
language modeling;NCE;self-normalization
| null | 0 | null | null |
iclr
| -0.720577 | 0 | null |
main
| 3.666667 |
2;3;6
| null | null |
A Matrix Approximation View of NCE that Justifies Self-Normalization
| null | null | 0 | 4 |
Withdraw
|
4;5;3
| null |
null |
University of Cambridge; University of Cambridge, Uber AI Labs
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Alexander Matthews, Jiri Hron, Mark Rowland, Richard E Turner, Zoubin Ghahramani
|
https://iclr.cc/virtual/2018/poster/161
|
Gaussian Processes;Bayesian Deep Learning;Theory of Deep Neural Networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Gaussian Process Behaviour in Wide Deep Neural Networks
|
https://github.com/widedeepnetworks/widedeepnetworks
| null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
invariance;cnn;gan;infogan;transformation
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3.333333 |
2;4;4
| null | null |
Parametrizing filters of a CNN with a GAN
| null | null | 0 | 4.333333 |
Reject
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
graph convolutional neural networks;graph-structured data;semi-classification
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Topology Adaptive Graph Convolutional Networks
| null | null | 0 | 3.666667 |
Reject
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning theory;architecture selection;algebraic topology
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
On Characterizing the Capacity of Neural Networks Using Algebraic Topology
| null | null | 0 | 5 |
Reject
|
5;5;5
| null |
null |
Product Architecture Group, Intel, OR; Parallel Computing Lab, Intel Labs, SC; Parallel Computing Lab, Intel Labs, India; Software Services Group, Intel, OR
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Dipankar Das, Naveen Mellempudi, Dheevatsa Mudigere, Dhiraj Kalamkar, Sasikanth Avancha, Kunal Banerjee, Srinivas Sridharan, Karthik Vaidyanathan, Bharat Kaul, Evangelos Georganas, Alexander Heinecke, Pradeep K Dubey, Jesus Corbal, Nikita Shustrov, Roma Dubtsov, Evarist Fomenko, Vadim Pirogov
|
https://iclr.cc/virtual/2018/poster/52
|
deep learning training;reduced precision;imagenet;dynamic fixed point
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Mixed Precision Training of Convolutional Neural Networks using Integer Operations
| null | null | 0 | 3.333333 |
Poster
|
3;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Label Propagation;Depthwise separable convolution;Graph and geometric convolution
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Learning Graph Convolution Filters from Data Manifold
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
distributed;deep learning;straggler
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 3.666667 |
3;4;4
| null | null |
Faster Distributed Synchronous SGD with Weak Synchronization
| null | null | 0 | 4.666667 |
Reject
|
4;5;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
GAN;graphs;random walks;implicit generative models
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
GraphGAN: Generating Graphs via Random Walks
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Department of Computer Science, ETH Zurich
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Yannic Kilcher, Aurelien Lucchi, Thomas Hofmann
|
https://iclr.cc/virtual/2018/poster/117
|
Deep Generative Models;GANs
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Semantic Interpolation in Implicit Models
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
sequence-to-sequence recurrent networks;compositionality;systematicity;generalization;language-driven navigation
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Still not systematic after all these years: On the compositional skills of sequence-to-sequence recurrent networks
| null | null | 0 | 4 |
Workshop
|
3;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Nuisance variation;transform learning;image embeddings
| null | 0 | null | null |
iclr
| -0.755929 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Correcting Nuisance Variation using Wasserstein Distance
| null | null | 0 | 3.666667 |
Reject
|
5;3;3
| null |
null |
Facebook AI Research; Sorbonne Universités, UPMC Univ Paris 06, UMR 7606, LIP6
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Guillaume Lample, , Marc'Aurelio Ranzato, , Hervé Jégou
|
https://iclr.cc/virtual/2018/poster/336
|
unsupervised learning;machine translation;multilingual embeddings;parallel dictionary induction;adversarial training
| null | 0 | null | null |
iclr
| -0.777714 | 0 | null |
main
| 6.666667 |
3;8;9
| null | null |
Word translation without parallel data
|
https://github.com/facebookresearch/MUSE
| null | 0 | 4 |
Poster
|
5;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;machine learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates
| null | null | 0 | 3.333333 |
Reject
|
3;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep Learning;Robotics;Artificial Intelligence;Computer Vision
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 2.333333 |
2;2;3
| null | null |
TOWARDS ROBOT VISION MODULE DEVELOPMENT WITH EXPERIENTIAL ROBOT LEARNING
| null | null | 0 | 3.666667 |
Reject
|
3;4;4
| null |
null |
University of California, Irvine, CA 92697, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Zhengli Zhao, Dheeru Dua, Sameer Singh
|
https://iclr.cc/virtual/2018/poster/142
|
adversarial examples;generative adversarial networks;interpretability;image classification;textual entailment;machine translation
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Generating Natural Adversarial Examples
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Lifelong learning;meta learning;word embedding
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Lifelong Word Embedding via Meta-Learning
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Batch Normalized;Convolutional Neural Networks;Displaced Rectifier Linear Unit;Comparative Study
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Enhancing Batch Normalized Convolutional Networks using Displaced Rectifier Linear Units: A Systematic Comparative Study
| null | null | 0 | 4.666667 |
Reject
|
5;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
program induction;HCI;deep learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
Learning to Infer Graphics Programs from Hand-Drawn Images
| null | null | 0 | 3.333333 |
Reject
|
4;2;4
| null |
null |
New York University, New York, NY 10003; New York Genome Center, New York, NY 10003, USA; Weill Cornell Medicine, Meyer Cancer Center, New York, NY 10065; Tri-Institutional Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY 10065; Weill Cornell Medicine, Division of Hematology and Medical Oncology, New York, NY 10065
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
somatic mutation;variant calling;cancer;liquid biopsy;early detection;convolution;deep learning;machine learning;lung cancer;error suppression;mutect
| null | 0 | null | null |
iclr
| 0.693375 | 0 | null |
main
| 5.666667 |
4;5;8
| null | null |
Deep learning mutation prediction enables early stage lung cancer detection in liquid biopsy
| null | null | 0 | 3.666667 |
Workshop
|
3;4;4
| null |
null |
DeepMind
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Daniel Horgan, John Quan, David Budden, Gabriel Barth-maron, Matteo Hessel, Hado van Hasselt, David Silver
|
https://iclr.cc/virtual/2018/poster/134
|
deep learning;reinforcement learning;distributed systems
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 7.333333 |
6;7;9
| null | null |
Distributed Prioritized Experience Replay
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Deep learning;model compression
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 5.666667 |
5;6;6
| null | null |
WSNet: Learning Compact and Efficient Networks with Weight Sampling
| null | null | 0 | 4 |
Workshop
|
5;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
TESLA: Task-wise Early Stopping and Loss Aggregation for Dynamic Neural Network Inference
| null | null | 0 | 2.666667 |
Reject
|
2;4;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Real time strategy;latent space;forward model;monte carlo tree search;reinforcement learning;planning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Latent forward model for Real-time Strategy game planning with incomplete information
| null | null | 0 | 4 |
Reject
|
5;3;4
| null |
null |
University of Toronto and Vector Institute
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Yuhuai Wu, Mengye Ren, Renjie Liao, Roger Grosse
|
https://iclr.cc/virtual/2018/poster/240
|
meta-learning; optimization; short-horizon bias.
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Understanding Short-Horizon Bias in Stochastic Meta-Optimization
|
https://github.com/renmengye/meta-optim-public
| null | 0 | 3.666667 |
Poster
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Object detection;Visual Tracking;Loss function;Region Proposal Network;Network compression
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Tracking Loss: Converting Object Detector to Robust Visual Tracker
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
neural network;reinforcement learning;natural language processing;machine translation;alpha-divergence
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4 |
4;4;4
| null | null |
Alpha-divergence bridges maximum likelihood and reinforcement learning in neural sequence generation
| null | null | 0 | 3 |
Reject
|
1;5;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 4 |
3;4;5
| null | null |
Post-training for Deep Learning
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
hyper-parameters;optimization
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 4.333333 |
4;4;5
| null | null |
Online Hyper-Parameter Optimization
| null | null | 0 | 3 |
Reject
|
3;3;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
exploration;intrinsic motivation;reinforcement learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Curiosity-driven Exploration by Bootstrapping Features
| null | null | 0 | 0 |
Withdraw
| null | null |
null |
Microsoft Research, Montreal; Unknown; Element AI, Montreal; Montreal Institute for Learning Algorithms (MILA), Montreal
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Chiheb Trabelsi, Olexa Bilaniuk, Ying Zhang, Dmitriy Serdyuk, Sandeep Subramanian, Joao Felipe Santos, Soroush Mehri, Negar Rostamzadeh, Yoshua Bengio, Christopher Pal
|
https://iclr.cc/virtual/2018/poster/2
|
deep learning;complex-valued neural networks
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
4;7;8
| null | null |
Deep Complex Networks
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
interpreting convolutional neural networks;nearest neighbors;generative adversarial networks
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 3.333333 |
3;3;4
| null | null |
Do Convolutional Neural Networks act as Compositional Nearest Neighbors?
| null | null | 0 | 4.333333 |
Withdraw
|
3;5;5
| null |
null |
Baidu Research, Sunnyvale USA; National Engineering Laboratory for Deep Learning Technology and Applications, Beijing China
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Haonan Yu, Haichao Zhang, Wei Xu
|
https://iclr.cc/virtual/2018/poster/275
|
grounded language learning and generalization;zero-shot language learning
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Interactive Grounded Language Acquisition and Generalization in a 2D World
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
kernel methods;low-rank approximation;quadrature rules;random features
| null | 0 | null | null |
iclr
| 0.981981 | 0 | null |
main
| 5.666667 |
4;6;7
| null | null |
Quadrature-based features for kernel approximation
| null | null | 0 | 4 |
Reject
|
3;4;5
| null |
null |
Facebook Research; Coordinated Science Lab, Department of ECE, University of Illinois at Urbana-Champaign
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
R. Srikant, Shiyu Liang, Yixuan Li
|
https://iclr.cc/virtual/2018/poster/264
|
Neural networks;out-of-distribution detection
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 7 |
6;6;9
| null | null |
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null |
Facebook AI Research & The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel; The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Tomer Galanti, Lior Wolf, Sagie Benaim
|
https://iclr.cc/virtual/2018/poster/154
|
Unsupervised learning;cross-domain mapping;Kolmogorov complexity;Occam's razor
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
The Role of Minimal Complexity Functions in Unsupervised Learning of Semantic Mappings
| null | null | 0 | 3.333333 |
Poster
|
4;2;4
| null |
null |
Toyota Technological Institute at Chicago, Chicago, IL, 60637, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Lifu Tu, Kevin Gimpel
|
https://iclr.cc/virtual/2018/poster/75
|
Approximate Inference Networks;Structured Prediction;Multi-Label Classification;Sequence Labeling
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7 |
5;7;9
| null | null |
Learning Approximate Inference Networks for Structured Prediction
| null | null | 0 | 4 |
Poster
|
3;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
multitask learning;lifelong learning;online learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 3 |
2;3;4
| null | null |
Lifelong Learning with Output Kernels
| null | null | 0 | 4.333333 |
Reject
|
5;4;4
| null |
null |
Deepmind; Google; University of Oxford; Microsoft Research
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Rudy Bunel, Matthew Hausknecht, Jacob Devlin, Rishabh Singh, Pushmeet Kohli
|
https://iclr.cc/virtual/2018/poster/294
|
Program Synthesis;Reinforcement Learning;Language Model
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis
| null | null | 0 | 3 |
Poster
|
3;3;3
| null |
null |
OpenAI; University of Amsterdam, TNO, Intelligent Imaging; University of Amsterdam, CIFAR
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Christos Louizos, Max Welling, Diederik Kingma
|
https://iclr.cc/virtual/2018/poster/222
|
Sparsity;compression;hard and soft attention.
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Learning Sparse Neural Networks through L_0 Regularization
| null | null | 0 | 3.333333 |
Poster
|
3;3;4
| null |
null |
ETH Zürich
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Paulina Grnarova, Kfir Y Levy, Aurelien Lucchi, Thomas Hofmann, Andreas Krause
|
https://iclr.cc/virtual/2018/poster/301
|
Generative Adversarial Networks;GANs;online learning
| null | 0 | null | null |
iclr
| 0.755929 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
An Online Learning Approach to Generative Adversarial Networks
| null | null | 0 | 4.333333 |
Poster
|
4;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Recurrent neural network;Vanishing and exploding gradients;Parameter efficiency;Kronecker matrices;Soft unitary constraint
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6 |
5;6;7
| null | null |
Kronecker Recurrent Units
| null | null | 0 | 4 |
Workshop
|
4;5;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
distributed representation;sentence embedding;structure;technical documents;sentence embedding;out-of-vocabulary
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
UNSUPERVISED SENTENCE EMBEDDING USING DOCUMENT STRUCTURE-BASED CONTEXT
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Stanford University; Intel Labs
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Ozan Sener, Silvio Savarese
|
https://iclr.cc/virtual/2018/poster/194
|
Active Learning;Convolutional Neural Networks;Core-Set Selection
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Active Learning for Convolutional Neural Networks: A Core-Set Approach
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Multitask learning;computer vision;multitask loss function
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4.666667 |
4;4;6
| null | null |
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
| null | null | 0 | 3.333333 |
Reject
|
4;4;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
softmax;optimization;implicit sgd
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
5;5;5
| null | null |
Unbiased scalable softmax optimization
| null | null | 0 | 3.666667 |
Reject
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Reinforcement learning;Q-learning;ensemble method;upper confidence bound
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6 |
5;6;7
| null | null |
UCB EXPLORATION VIA Q-ENSEMBLES
| null | null | 0 | 4 |
Reject
|
3;5;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
convolution neural networks;attention;music information retrieval
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 0 | null | null | null |
Learning Audio Features for Singer Identification and Embedding
| null | null | 0 | 0 |
Withdraw
| null | null |
null |
Centre for Artificial Intelligence, School of Software, University of Technology Sydney; Paul G. Allen School of Computer Science & Engineering, University of Washington
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang
|
https://iclr.cc/virtual/2018/poster/234
|
deep learning;attention mechanism;sequence modeling;natural language processing;sentence embedding
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
6;6;9
| null | null |
Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling
| null | null | 0 | 4 |
Poster
|
4;4;4
| null |
null |
University of Oxford, United Kingdom
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Tim Rocktäschel
|
https://iclr.cc/virtual/2018/poster/198
|
reinforcement learning;deep learning;planning
| null | 0 | null | null |
iclr
| 0.27735 | 0 | null |
main
| 5.666667 |
4;5;8
| null | null |
TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning
| null | null | 0 | 4.333333 |
Poster
|
5;3;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reading comprehension;question answering
| null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension
| null | null | 0 | 3.666667 |
Workshop
|
4;3;4
| null |
null |
IBM Research AI, Yorktown Heights, NY
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Abhishek Kumar, Prasanna Sattigeri, Avinash Balakrishnan
|
https://iclr.cc/virtual/2018/poster/82
|
disentangled representations;variational inference
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Variational Inference of Disentangled Latent Concepts from Unlabeled Observations
| null | null | 0 | 4.333333 |
Poster
|
5;4;4
| null |
null |
N/A
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
reinforcement learning;pretrained;deep learning;perception;algorithmic
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 3 |
2;3;4
| null | null |
Sequential Coordination of Deep Models for Learning Visual Arithmetic
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
natural gradient;generalization;optimization;function space;Hilbert
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5 |
4;5;6
| null | null |
Improving generalization by regularizing in $L^2$ function space
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null |
Computer Science, University of Texas at Austin, Austin, TX, 78712; Google, Kirkland, WA, 98033; Microsoft, Redmond, WA, 98052; Computer Science, UESTC, Chengdu, China; Computer Science, UIUC, Urbana, IL 61801; Computer science, University of Texas at Austin, Austin, TX, 78712
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Hao Liu, Yihao Feng, Yi Mao, Dengyong Zhou, Jian Peng, Qiang Liu
|
https://iclr.cc/virtual/2018/poster/106
|
reinforcement learning;control variates;sample efficiency;variance reduction
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 7 |
7;7;7
| null | null |
Action-dependent Control Variates for Policy Optimization via Stein Identity
| null | null | 0 | 3.333333 |
Poster
|
4;3;3
| null |
null |
Salesforce Research, Palo Alto, CA 94301, USA
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Caiming Xiong, richard socher, Victor Zhong
|
https://iclr.cc/virtual/2018/poster/258
|
question answering;deep learning;natural language processing;reinforcement learning
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
DCN+: Mixed Objective And Deep Residual Coattention for Question Answering
| null | null | 0 | 3.333333 |
Poster
|
4;4;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning theory;infinite neural networks;topology
| null | 0 | null | null |
iclr
| -0.995871 | 0 | null |
main
| 4.666667 |
3;4;7
| null | null |
Deep Function Machines: Generalized Neural Networks for Topological Layer Expression
| null | null | 0 | 2.666667 |
Reject
|
4;3;1
| null |
null |
Stanford University; DeepMind
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Rui Shu, Hung H Bui, Hirokazu Narui, Stefano Ermon
|
https://iclr.cc/virtual/2018/poster/26
|
domain adaptation;unsupervised learning;semi-supervised learning
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 7.333333 |
7;7;8
| null | null |
A DIRT-T Approach to Unsupervised Domain Adaptation
|
https://github.com/RuiShu/dirt-t
| null | 0 | 3.333333 |
Poster
|
2;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
information theory;generative models;latent variable models;variational autoencoders
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 5.666667 |
5;5;7
| null | null |
An information-theoretic analysis of deep latent-variable models
| null | null | 0 | 4.666667 |
Reject
|
4;5;5
| null |
null |
Amazon Web Services; University of Illinois at Urbana-Champaign
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Ashish Khetan, Zachary Lipton, anima anandkumar
|
https://iclr.cc/virtual/2018/poster/158
|
crowdsourcing;noisy annotations;deep leaerning
| null | 0 | null | null |
iclr
| 1 | 0 | null |
main
| 6.666667 |
6;7;7
| null | null |
Learning From Noisy Singly-labeled Data
| null | null | 0 | 3.666667 |
Poster
|
3;4;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
deep learning;experimental analysis;hidden neurons
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 5.333333 |
4;5;7
| null | null |
Discovering the mechanics of hidden neurons
| null | null | 0 | 4 |
Reject
|
4;4;4
| null |
null |
Department of EECS, UC Berkeley; OpenAI; Institute for Transportation Studies, UC Berkeley; Department of CSE, University of Washington
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Cathy Wu, Aravind Rajeswaran, Yan Duan, Vikash Kumar, Alexandre M Bayen, Sham M Kakade, Igor Mordatch, Pieter Abbeel
|
https://iclr.cc/virtual/2018/poster/115
|
reinforcement learning;policy gradient;variance reduction;baseline;control variates
| null | 0 | null | null |
iclr
| -0.866025 | 0 | null |
main
| 7 |
6;7;8
| null | null |
Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines
| null | null | 0 | 3.666667 |
Oral
|
4;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
active inference;predictive coding;motor control
| null | 0 | null | null |
iclr
| -0.944911 | 0 | null |
main
| 3.666667 |
3;3;5
| null | null |
Toward predictive machine learning for active vision
| null | null | 0 | 3.666667 |
Reject
|
5;4;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
medical diagnosis;medical imaging;multi-label classification
| null | 0 | null | null |
iclr
| 0 | 0 | null |
main
| 6 |
6;6;6
| null | null |
Learning to diagnose from scratch by exploiting dependencies among labels
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null |
UC Irvine; Amazon AI, Imperial College London; Amazon AI, Caltech; Amazon AI; Amazon AI, UT Austin; Amazon AI, CMU
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Guneet Dhillon, Kamyar Azizzadenesheli, Zachary Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, anima anandkumar
|
https://iclr.cc/virtual/2018/poster/71
| null | null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.333333 |
6;6;7
| null | null |
Stochastic Activation Pruning for Robust Adversarial Defense
| null | null | 0 | 3.666667 |
Poster
|
4;3;4
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null | null | null | 0 | null | null |
iclr
| -0.5 | 0 | null |
main
| 3.666667 |
3;3;5
| null | null |
Interpreting Deep Classification Models With Bayesian Inference
| null | null | 0 | 3.333333 |
Reject
|
4;3;3
| null |
null |
New York University; New York University, Facebook AI Research; Facebook AI Research
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Jason Lee, Kyunghyun Cho, Jason Weston, Douwe Kiela
|
https://iclr.cc/virtual/2018/poster/219
| null | null | 0 | null | null |
iclr
| -0.188982 | 0 | null |
main
| 6.666667 |
5;7;8
| null | null |
Emergent Translation in Multi-Agent Communication
| null | null | 0 | 4.333333 |
Poster
|
5;3;5
| null |
null |
University of California, Berkeley; OpenAI
|
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
machine teaching;interpretability;communication;cognitive science
| null | 0 | null | null |
iclr
| 0.5 | 0 | null |
main
| 6.666667 |
4;8;8
| null | null |
Interpretable and Pedagogical Examples
| null | null | 0 | 3.333333 |
Reject
|
3;4;3
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Adversarial Attack;Interpretability;Saliency Map;Influence Function;Robustness;Machine Learning;Deep Learning;Neural Network
| null | 0 | null | null |
iclr
| -0.654654 | 0 | null |
main
| 5 |
4;5;6
| null | null |
INTERPRETATION OF NEURAL NETWORK IS FRAGILE
| null | null | 0 | 3.666667 |
Reject
|
4;5;2
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
computer vision;scene understanding;text processing
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 4 |
2;5;5
| null | null |
pix2code: Generating Code from a Graphical User Interface Screenshot
|
https://github.com/Anonymous
| null | 0 | 4.333333 |
Withdraw
|
5;4;4
| null |
null |
Department of Computer Science, University of Texas at Austin; Department of Computer Science, Cornell University; Princeton University and Google Brain
|
2018
| 0 | null | null | 0 | null | null | null | null | null |
Elad Hazan, Adam Klivans, Yang Yuan
|
https://iclr.cc/virtual/2018/poster/280
|
Hyperparameter Optimization;Fourier Analysis;Decision Tree;Compressed Sensing
| null | 0 | null | null |
iclr
| 0.866025 | 0 | null |
main
| 7 |
6;6;9
| null | null |
Hyperparameter optimization: a spectral approach
| null | null | 0 | 4 |
Poster
|
3;4;5
| null |
null | null |
2018
| 0 | null | null | 0 | null | null | null | null | null | null | null |
Generative models;Evaluation of generative models;Data Augmentation
| null | 0 | null | null |
iclr
| -1 | 0 | null |
main
| 3.666667 |
3;3;5
| null | null |
Evaluation of generative networks through their data augmentation capacity
| null | null | 0 | 4.333333 |
Reject
|
5;5;3
| null |
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