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2025 ICML ICML 2025

Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment

Abstract

No abstract available. No abstract was captured for this paper when its proceedings were scraped - most often an older edition published without one online. How the corpus is built.

Authors

Harrish Thasarathan, Julian Forsyth, Thomas FEL, Matthew Kowal, Konstantinos G. Derpanis

Topics

Artificial Intelligence > Core AI > Interpretability Machine Learning > Core Methods > Representation Learning Deep Learning > Architectures > Neural Networks

Keywords

autoencoders concept alignment activation reconstruction cross model interpretable sparse
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