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cs.LG2024
Compute Optimal Inference and Provable Amortisation Gap in Sparse Autoencoders
Charles O'Neill, Alim Gumran, David Klindt
A recent line of work has shown promise in using sparse autoencoders (SAEs) to uncover interpretable features in neural network representations. However, the simple linear-nonlinea…
cs.LG2024★ 3 cited
Disentangling Dense Embeddings with Sparse Autoencoders
Charles O'Neill, Christine Ye, Kartheik Iyer +1
Sparse autoencoders (SAEs) have shown promise in extracting interpretable features from complex neural networks. We present one of the first applications of SAEs to dense text embe…