2 citations · 3 across the 4 of their papers we have counts for
4 papers
How Optimality Structures Sparse Dictionaries: Theory for Interpreting SAE Representations
William Dorrell
Sparse Autoencoders (SAEs) have found success parsing neural network representations into interpretable concepts, providing a basis for understanding and control. However, what exa…
Convex Efficient Coding
William Dorrell, Peter E. Latham, James Whittington
Why do neurons encode information the way they do? Normative answers to this question model neural activity as the solution to an optimisation problem; for example, the celebrated…
Range, not Independence, Drives Modularity in Biologically Inspired Representations
Will Dorrell, Kyle Hsu, Luke Hollingsworth +6
Why do biological and artificial neurons sometimes modularise, each encoding a single meaningful variable, and sometimes entangle their representation of many variables? In this wo…
Disentanglement via Latent Quantization
Kyle Hsu, Will Dorrell, James C. R. Whittington +2
In disentangled representation learning, a model is asked to tease apart a dataset's underlying sources of variation and represent them independently of one another. Since the mode…