README.md

metadata

license: mit
task_categories:
  - other
language:
  - en
tags:
  - quantum
  - neural-networks
  - belief-states
  - transformers
  - rnn
size_categories:
  - 1B<n<10B

Epsilon-Transformers Belief Analysis Dataset

This dataset contains trained neural network models and their corresponding belief state regression analysis from the Epsilon-Transformers project. The models were trained on four different stochastic processes and analyzed for their ability to learn and represent belief states.

Dataset Structure

epsilon-transformers-belief-analysis/
├── README.md
├── models/          # Model checkpoints and configurations from S3
│   ├── {sweep_id}_{run_id}/
│   │   ├── 0.pt                    # Initial checkpoint
│   │   ├── {final}.pt              # Final checkpoint
│   │   ├── run_config.yaml         # Training configuration
│   │   └── loss.csv                # Training loss data
│   └── ...
└── analysis/        # Belief state regression analysis results
    ├── {sweep_id}_{run_id}/
    │   ├── checkpoint_0.joblib              # Initial checkpoint analysis
    │   ├── checkpoint_{final}.joblib        # Final checkpoint analysis
    │   ├── ground_truth_data.joblib         # Neural network ground truth
    │   ├── markov3_checkpoint_*.joblib      # Classical Markov comparisons
    │   └── markov3_ground_truth_data.joblib # Classical ground truth
    └── ...

Model Mappings

Sweep ID	Run ID	Architecture	Process	Description
20241121152808	48	LSTM	Moon Process	LSTM trained on Moon Process
20241121152808	49	LSTM	Bloch Walk	LSTM trained on Bloch Walk
20241121152808	53	LSTM	FRDN	LSTM trained on FRDN
20241121152808	55	LSTM	Mess3	LSTM trained on Mess3
20241121152808	56	GRU	Moon Process	GRU trained on Moon Process
20241121152808	57	GRU	Bloch Walk	GRU trained on Bloch Walk
20241121152808	61	GRU	FRDN	GRU trained on FRDN
20241121152808	63	GRU	Mess3	GRU trained on Mess3
20241121152808	64	RNN	Moon Process	RNN trained on Moon Process
20241121152808	65	RNN	Bloch Walk	RNN trained on Bloch Walk
20241121152808	69	RNN	FRDN	RNN trained on FRDN
20241121152808	71	RNN	Mess3	RNN trained on Mess3
20241205175736	17	Transformer	Bloch Walk	Transformer trained on Bloch Walk
20241205175736	23	Transformer	Mess3	Transformer trained on Mess3
20250421221507	0	Transformer	Moon Process	Transformer trained on Moon Process
20250422023003	1	Transformer	FRDN	Transformer trained on FRDN

Process Descriptions

Mess3 (Classical Process)

A classical stochastic process used as a baseline for comparison with quantum processes.

FRDN (Finite Random Dynamics Networks)

A quantum process representing finite random dynamics networks, modeling quantum systems with specific structural properties.

Bloch Walk

A quantum random walk process on the Bloch sphere, representing quantum state evolution in a geometric framework.

Moon Process

A post-quantum stochastic process that explores computational mechanics beyond standard quantum frameworks.

Model Architectures

RNN Models (LSTM, GRU, RNN)

• Layers: 4
• Hidden Units: 64
• Direction: Unidirectional
• Configuration: L4_H64_uni

Transformer Models

• Layers: 4
• Attention Heads: 4
• Head Dimension: 16
• Model Dimension: 64
• Configuration: L4_H4_DH16_DM64

File Formats

Model Files (.pt)

PyTorch model checkpoints containing trained model weights and optimizer states.

Analysis Files (.joblib)

Joblib-serialized files containing:

• checkpoint_*.joblib: Regression analysis results mapping activations to belief states
• ground_truth_data.joblib: True belief states and probabilities for the neural network data
• markov3_*.joblib: Classical Markov model comparisons and baselines

Usage

Loading Models

import torch
from pathlib import Path

# Load a model checkpoint
model_path = Path("models/20241121152808_57/4075724800.pt")
checkpoint = torch.load(model_path, map_location='cpu')

Loading Analysis Data

import joblib
from pathlib import Path

# Load regression analysis results
analysis_path = Path("analysis/20241121152808_57/checkpoint_4075724800.joblib")
analysis_data = joblib.load(analysis_path)

# Access layer-wise regression metrics
for layer, metrics in analysis_data.items():
    print(f"Layer {layer} RMSE: {metrics['rmse']}")

Citation

If you use this dataset in your research, please cite:

@misc{epsilon-transformers-belief-analysis,
  title={Epsilon-Transformers Belief Analysis Dataset},
  author={[Your Name]},
  year={2024},
  howpublished={Hugging Face Datasets},
  url={https://huggingface.co/datasets/[your-username]/epsilon-transformers-belief-analysis}
}

License

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Contact

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