
Understandable Machine Intelligence Lab
@UMI_Lab_AI
Understandable Machine Intelligence Lab: We bring #explainable #AI to the next level. Part of @LeibnizATB, Ex @TUBerlin, funded by @BMBF_Bund #XAI
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Happy to share that our PRISM paper has been accepted at #NeurIPS2025 🎉 In this work, we introduce a multi-concept feature description framework that can identify and score polysemantic features. 📄 Paper: arxiv.org/abs/2506.15538 #NeurIPS #MechInterp #XAI
Our latest paper is out! 🚀
🔍 When do neurons encode multiple concepts? We introduce PRISM, a framework for extracting multi-concept feature descriptions to better understand polysemanticity. 📄 Capturing Polysemanticity with PRISM: A Multi-Concept Feature Description Framework arxiv.org/abs/2506.15538 🧵

If you're at #AAAI2025 don't miss our poster today (alignment track)! Paper 📘: arxiv.org/pdf/2502.15403 Code 👩💻: github.com/annahedstroem/… Team work with @eirasf and @Marina_MCV
At 12:30 I'll be happy to take questions about our poster presentation at #AAAI2025. Is your explanation for a model's prediction better than the alternatives? "Evaluate with the Inverse: Efficient Approximation of Latent Explanation Quality Distribution" introduces QGE... 1/4
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