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cs.LG2024★ 4 cited
Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders
Luke Marks, Alasdair Paren, David Krueger +1
Sparse Autoencoders (SAEs) have shown promise in improving the interpretability of neural network activations, but can learn features that are not features of the input, limiting t…
cs.LG2024
Informal Safety Guarantees for Simulated Optimizers Through Extrapolation from Partial Simulations
Luke Marks
Self-supervised learning is the backbone of state of the art language modeling. It has been argued that training with predictive loss on a self-supervised dataset causes simulators…