metadata
tags:
- weight-space-learning
- neural-network-autoencoder
- autoencoder
- transformer
datasets:
- maximuspowers/muat-fourier-5
Weight-Space Autoencoder (TRANSFORMER)
This model is a weight-space autoencoder trained on neural network activation weights/signatures. It includes both an encoder (compresses weights into latent representations) and a decoder (reconstructs weights from latent codes).
Model Description
- Architecture: Transformer encoder-decoder
- Training Dataset: maximuspowers/muat-fourier-5
- Input Mode: signature
- Latent Dimension: 256
Tokenization
- Chunk Size: 64 weight values per token
- Max Tokens: 512
- Metadata: True
Training Config
- Loss Function: contrastive
- Optimizer: adam
- Learning Rate: 0.0001
- Batch Size: 8
Performance Metrics (Test Set)
- MSE: 0.088272
- MAE: 0.213148
- RMSE: 0.297107
- Cosine Similarity: 0.8509
- R² Score: 0.7242