1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2026
A Geometric View for Understanding Concept Learning and Neuron Interpretation in Sparse Autoencoders
Chenhao Zhang, Chris Lin, Su-In Lee
We propose a unified mathematical framework for a geometric understanding of concept learning and neuron interpretation in sparse autoencoders (SAEs). While SAEs improve interpreta…
cs.LG2024★ 1 cited
Stochastic Amortization: A Unified Approach to Accelerate Feature and Data Attribution
Ian Covert, Chanwoo Kim, Su-In Lee +2
Many tasks in explainable machine learning, such as data valuation and feature attribution, perform expensive computation for each data point and are intractable for large datasets…
cs.LG2023
Feature Selection in the Contrastive Analysis Setting
Ethan Weinberger, Ian Covert, Su-In Lee
Contrastive analysis (CA) refers to the exploration of variations uniquely enriched in a target dataset as compared to a corresponding background dataset generated from sources of…