4 citations · 6 across the 12 of their papers we have counts for
3 papers · 1 filter
Probing the Representational Power of Sparse Autoencoders in Vision Models
Matthew Lyle Olson, Musashi Hinck, Neale Ratzlaff +4
Sparse Autoencoders (SAEs) have emerged as a popular tool for interpreting the hidden states of large language models (LLMs). By learning to reconstruct activations from a sparse b…
Analyzing Hierarchical Structure in Vision Models with Sparse Autoencoders
Matthew Lyle Olson, Musashi Hinck, Neale Ratzlaff +4
The ImageNet hierarchy provides a structured taxonomy of object categories, offering a valuable lens through which to analyze the representations learned by deep vision models. In…
Probing Semantic Routing in Large Mixture-of-Expert Models
Matthew Lyle Olson, Neale Ratzlaff, Musashi Hinck +4
In the past year, large (>100B parameter) mixture-of-expert (MoE) models have become increasingly common in the open domain. While their advantages are often framed in terms of eff…