Andrea Dittadi
@andreadittadi.bsky.social
postdoc at Helmholtz AI & TUM | diffusion/flows & (causal) representation learning | previously DTU Copenhagen, MPI Tübingen, Microsoft Research, Amazon
addtt.github.io
addtt.github.io
Reposted by Andrea Dittadi
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 👇
Details below 👇
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...
arxiv.org
October 21, 2025 at 5:45 AM
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 👇
Details below 👇
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/
We’ll present our new work exploring these challenges next week at @unireps #NeurIPS2024 🇨🇦🎉
openreview.net/pdf?id=SQKUZSieVg
1/
openreview.net
December 6, 2024 at 4:06 PM
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/
We’ll present our new work exploring these challenges next week at @unireps #NeurIPS2024 🇨🇦🎉
openreview.net/pdf?id=SQKUZSieVg
1/