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cs.LG2025
Data Whitening Improves Sparse Autoencoder Learning
Ashwin Saraswatula, David Klindt
Sparse autoencoders (SAEs) have emerged as a promising approach for learning interpretable features from neural network activations. However, the optimization landscape for SAE tra…
cs.LG2025
From superposition to sparse codes: interpretable representations in neural networks
David Klindt, Charles O'Neill, Patrik Reizinger +2
Understanding how information is represented in neural networks is a fundamental challenge in both neuroscience and artificial intelligence. Despite their nonlinear architectures,…
cs.LG2025
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…