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cs.LG2024
Adaptive Sparse Allocation with Mutual Choice & Feature Choice Sparse Autoencoders
Kola Ayonrinde
Sparse autoencoders (SAEs) are a promising approach to extracting features from neural networks, enabling model interpretability as well as causal interventions on model internals.…
cs.LG2024
Interpretability as Compression: Reconsidering SAE Explanations of Neural Activations with MDL-SAEs
Kola Ayonrinde, Michael T. Pearce, Lee Sharkey
Sparse Autoencoders (SAEs) have emerged as a useful tool for interpreting the internal representations of neural networks. However, naively optimising SAEs for reconstruction loss…