README.md

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

Mess3

| 20241121152808 | 55 | LSTM | Mess3 | LSTM trained on Mess3 | | 20241121152808 | 63 | GRU | Mess3 | GRU trained on Mess3 | | 20241121152808 | 71 | RNN | Mess3 | RNN trained on Mess3 | | 20241205175736 | 23 | Transformer | Mess3 | Transformer trained on Mess3 |

FRDN

| 20241121152808 | 53 | LSTM | FRDN | LSTM trained on FRDN | | 20241121152808 | 61 | GRU | FRDN | GRU trained on FRDN | | 20241121152808 | 69 | RNN | FRDN | RNN trained on FRDN | | 20250422023003 | 1 | Transformer | FRDN | Transformer trained on FRDN |

Bloch Walk

| 20241121152808 | 49 | LSTM | Bloch Walk | LSTM trained on Bloch Walk | | 20241121152808 | 57 | GRU | Bloch Walk | GRU trained on Bloch Walk | | 20241121152808 | 65 | RNN | Bloch Walk | RNN trained on Bloch Walk | | 20241205175736 | 17 | Transformer | Bloch Walk | Transformer trained on Bloch Walk |

Moon Process

| 20241121152808 | 48 | LSTM | Moon Process | LSTM trained on Moon Process | | 20241121152808 | 56 | GRU | Moon Process | GRU trained on Moon Process | | 20241121152808 | 64 | RNN | Moon Process | RNN trained on Moon Process | | 20250421221507 | 0 | Transformer | Moon Process | Transformer trained on Moon Process |

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

[Specify your license here]

Contact

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