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sig-autoencoder-fourier-5-simclr-mse – AI Model by maximuspowers | AlphaNeural AI
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sig-autoencoder-fourier-5-simclr-mse
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tensorboard
weight-space-learning
neural-network-autoencoder
autoencoder
transformer
maximuspowers/muat-fourier-5
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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
