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Andrea Dittadi

@andreadittadi.bsky.social
73 followers 65 following 5 posts

postdoc at Helmholtz AI & TUM | diffusion/flows & (causal) representation learning | previously DTU Copenhagen, MPI Tübingen, Microsoft Research, Amazon addtt.github.io

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Reposted by Andrea Dittadi
Beatrix M. G. Nielsen @beatrixmgn.bsky.social · 21/10/2025
I am happy to announce that our article "When Does Closeness in Distribution Imply Representational Similarity? An Identifiability Perspective" has been accepted at NeurIPS 2025! 🎉 arxiv.org/abs/2506.037... Details below 👇
arxiv.org
When Does Closeness in Distribution Imply Representational Similarity? An Identifiability Perspective
When and why representations learned by different deep neural networks are similar is an active research topic. We choose to address these questions from the perspective of identifiability theory, whi...
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Andrea Dittadi @andreadittadi.bsky.social · 06/12/2024
Why do NNs often learn similar representations? Existing identifiability results offer theoretical insights, but applying them in practice poses challenges. We’ll present our new work exploring these challenges next week at @unireps #NeurIPS2024 🇨🇦🎉 openreview.net/pdf?id=SQKUZSieVg 1/
openreview.net
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