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Datasets:
SimplexAI
/
quantum-representations

Tasks:
Other
Modalities:
Tabular
Formats:
csv
Languages:
English
Size:
100K - 1M
Tags:
quantum
neural-networks
belief-states
transformers
rnn
Libraries:
Datasets
Dask
License:
Dataset card Data Studio Files Community
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README.md ADDED
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+ # Epsilon-Transformers Belief Analysis Dataset
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+
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+ 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.
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+
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+ ## Dataset Structure
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+
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+ ```
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+ epsilon-transformers-belief-analysis/
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+ ├── README.md
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+ ├── models/ # Model checkpoints and configurations from S3
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+ │ ├── {sweep_id}_{run_id}/
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+ │ │ ├── 0.pt # Initial checkpoint
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+ │ │ ├── {final}.pt # Final checkpoint
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+ │ │ ├── run_config.yaml # Training configuration
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+ │ │ └── loss.csv # Training loss data
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+ │ └── ...
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+ └── analysis/ # Belief state regression analysis results
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+ ├── {sweep_id}_{run_id}/
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+ │ ├── checkpoint_0.joblib # Initial checkpoint analysis
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+ │ ├── checkpoint_{final}.joblib # Final checkpoint analysis
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+ │ ├── ground_truth_data.joblib # Neural network ground truth
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+ │ ├── markov3_checkpoint_*.joblib # Classical Markov comparisons
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+ │ └── markov3_ground_truth_data.joblib # Classical ground truth
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+ └── ...
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+ ```
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+
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+ ## Model Mappings
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+
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+ | Sweep ID | Run ID | Architecture | Process | Description |
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+ |----------|--------|--------------|---------|-------------|
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+
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+ ### Mess3
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+ | 20241121152808 | 55 | LSTM | Mess3 | LSTM trained on Mess3 |
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+ | 20241121152808 | 63 | GRU | Mess3 | GRU trained on Mess3 |
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+ | 20241121152808 | 71 | RNN | Mess3 | RNN trained on Mess3 |
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+ | 20241205175736 | 23 | Transformer | Mess3 | Transformer trained on Mess3 |
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+
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+ ### FRDN
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+ | 20241121152808 | 53 | LSTM | FRDN | LSTM trained on FRDN |
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+ | 20241121152808 | 61 | GRU | FRDN | GRU trained on FRDN |
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+ | 20241121152808 | 69 | RNN | FRDN | RNN trained on FRDN |
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+ | 20250422023003 | 1 | Transformer | FRDN | Transformer trained on FRDN |
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+
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+ ### Bloch Walk
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+ | 20241121152808 | 49 | LSTM | Bloch Walk | LSTM trained on Bloch Walk |
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+ | 20241121152808 | 57 | GRU | Bloch Walk | GRU trained on Bloch Walk |
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+ | 20241121152808 | 65 | RNN | Bloch Walk | RNN trained on Bloch Walk |
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+ | 20241205175736 | 17 | Transformer | Bloch Walk | Transformer trained on Bloch Walk |
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+
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+ ### Moon Process
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+ | 20241121152808 | 48 | LSTM | Moon Process | LSTM trained on Moon Process |
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+ | 20241121152808 | 56 | GRU | Moon Process | GRU trained on Moon Process |
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+ | 20241121152808 | 64 | RNN | Moon Process | RNN trained on Moon Process |
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+ | 20250421221507 | 0 | Transformer | Moon Process | Transformer trained on Moon Process |
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+
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+
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+ ## Process Descriptions
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+
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+ ### Mess3 (Classical Process)
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+ A classical stochastic process used as a baseline for comparison with quantum processes.
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+
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+ ### FRDN (Finite Random Dynamics Networks)
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+ A quantum process representing finite random dynamics networks, modeling quantum systems with specific structural properties.
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+
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+ ### Bloch Walk
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+ A quantum random walk process on the Bloch sphere, representing quantum state evolution in a geometric framework.
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+
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+ ### Moon Process
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+ A post-quantum stochastic process that explores computational mechanics beyond standard quantum frameworks.
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+
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+ ## Model Architectures
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+
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+ ### RNN Models (LSTM, GRU, RNN)
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+ - **Layers**: 4
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+ - **Hidden Units**: 64
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+ - **Direction**: Unidirectional
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+ - **Configuration**: L4_H64_uni
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+
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+ ### Transformer Models
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+ - **Layers**: 4
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+ - **Attention Heads**: 4
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+ - **Head Dimension**: 16
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+ - **Model Dimension**: 64
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+ - **Configuration**: L4_H4_DH16_DM64
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+
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+ ## File Formats
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+
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+ ### Model Files (.pt)
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+ PyTorch model checkpoints containing trained model weights and optimizer states.
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+
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+ ### Analysis Files (.joblib)
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+ Joblib-serialized files containing:
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+ - **checkpoint_*.joblib**: Regression analysis results mapping activations to belief states
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+ - **ground_truth_data.joblib**: True belief states and probabilities for the neural network data
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+ - **markov3_*.joblib**: Classical Markov model comparisons and baselines
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+
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+ ## Usage
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+
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+ ### Loading Models
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+ ```python
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+ import torch
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+ from pathlib import Path
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+
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+ # Load a model checkpoint
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+ model_path = Path("models/20241121152808_57/4075724800.pt")
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+ checkpoint = torch.load(model_path, map_location='cpu')
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+ ```
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+
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+ ### Loading Analysis Data
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+ ```python
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+ import joblib
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+ from pathlib import Path
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+
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+ # Load regression analysis results
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+ analysis_path = Path("analysis/20241121152808_57/checkpoint_4075724800.joblib")
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+ analysis_data = joblib.load(analysis_path)
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+
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+ # Access layer-wise regression metrics
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+ for layer, metrics in analysis_data.items():
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+ print(f"Layer {layer} RMSE: {metrics['rmse']}")
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+ ```
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+
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+ ## Citation
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+
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+ If you use this dataset in your research, please cite:
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+
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+ ```bibtex
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+ @misc{epsilon-transformers-belief-analysis,
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+ title={Epsilon-Transformers Belief Analysis Dataset},
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+ author={[Your Name]},
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+ year={2024},
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+ howpublished={Hugging Face Datasets},
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+ url={https://huggingface.co/datasets/[your-username]/epsilon-transformers-belief-analysis}
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+ }
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+ ```
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+
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+ ## License
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+
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+ [Specify your license here]
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+
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+ ## Contact
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+
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+ [Your contact information]
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