🌌🛰️🔭Want to explore universal visual features? Check out our interactive demo of concepts learned from our #ICML2025 paper "Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment". Come see our poster at 4pm on Tuesday in East Exhibition hall A-B, E-1208!

Harry Thasarathan@hthasarathan.bsky.social · last yr.

🌌🛰️🔭Wanna know which features are universal vs unique in your models and how to find them? Excited to share our preprint: "Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment"! arxiv.org/abs/2502.03714 (1/9)

Our work finding universal concepts in vision models is accepted at #ICML2025!!! My first major conference paper with my wonderful collaborators and friends @matthewkowal.bsky.social @thomasfel.bsky.social @Julian_Forsyth @csprofkgd.bsky.social Working with y'all is the best 🥹 Preprint ⬇️!!

Harry Thasarathan@hthasarathan.bsky.social · last yr.

🌌🛰️🔭Wanna know which features are universal vs unique in your models and how to find them? Excited to share our preprint: "Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment"! arxiv.org/abs/2502.03714 (1/9)

Accepted at #ICML2025! Check out the preprint. HUGE shoutout to Harry (1st PhD paper, in 1st year), Julian (1st ever, done as an undergrad), Thomas and Matt! @hthasarathan.bsky.social @thomasfel.bsky.social @matthewkowal.bsky.social

Harry Thasarathan@hthasarathan.bsky.social · last yr.

🌌🛰️🔭Wanna know which features are universal vs unique in your models and how to find them? Excited to share our preprint: "Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment"! arxiv.org/abs/2502.03714 (1/9)

Check out Neehar Kondapaneni's upcoming ICLR 2025 work which proposes a new approach for understanding how two neural networks differ by discovering the shared and unique concepts learned by the networks. Representational Similarity via Interpretable Visual Concepts arxiv.org/abs/2503.15699

Neehar Kondapaneni@therealpaneni.bsky.social · last yr.

Have you ever wondered what makes two models different? We all know the ViT-Large performs better than the Resnet-50, but what visual concepts drive this difference? Our new ICLR 2025 paper addresses this question! nkondapa.github.io/rsvc-page/

A very interesting work that explores the possibility of having a unified interpretation across multiple models

Harry Thasarathan@hthasarathan.bsky.social · last yr.

🌌🛰️🔭Wanna know which features are universal vs unique in your models and how to find them? Excited to share our preprint: "Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment"! arxiv.org/abs/2502.03714 (1/9)

🌌🛰️🔭Wanna know which features are universal vs unique in your models and how to find them? Excited to share our preprint: "Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment"! arxiv.org/abs/2502.03714 (1/9)