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Lukas Muttenthaler

@lukas_mut

Senior ML Researcher @AIgnostics & @ExplainableML | ex-@GoogleDeepMind | PhD in ML from @TUBerlin @bifoldberlin @MPI_CBS | Representations in 🧠 & 🤖| #FirstGen
lukasmut.github.io Joined May 2019
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Lukas Muttenthaler @lukas_mut
🥳I am incredibly humbled and grateful to share that our work, "Aligning machine and human visual representations across abstraction levels," has been published today in @Nature: nature.com/articles/s4158… For more information see @AndrewLampinen’s post below ⬇️
www.nature.com Aligning machine and human visual representations across abstraction levels Nature - Aligning foundation models with human judgments enables them to more accurately approximate human behaviour and uncertainty across various levels of visual abstraction, while additionally...
Andrew Lampinen @AndrewLampinen Nov 12, 2025
What aspects of human knowledge do vision models like CLIP fail to capture, and how can we improve them? We suggest models miss key global organization; aligning them makes them more robust. Check out @lukas_mut's work, finally out (in @Nature!?) + our new blogpost! 1/4
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Lukas Muttenthaler @lukas_mut
🔮At #ICML2026 we presented Attentive Multi-Layer Fusion for Vision Transformers, an efficient method for fusing information from multiple layers of ViTs for downstream task predictions. It’s almost as performant as fine-tuning but only an inch more expensive than linear probing!
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Wed, Jul 8, 2026 • 10:30 AM – 12:15 PM KST · HALL A #2711 · POSTER
Attentive Multi-Layer Fusion for Vision Transformers
@lciernik, Marco Morik, @lukas_thede, @LucaEyring, Shinichi Nakajima, @zeynepakata, @lukas_mut
Paper: arxiv.org/abs/2601.09322
Code: github.com/lciernik/atten…
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Lukas Muttenthaler @lukas_mut
🚨Go check out our most recent preprint on multilayer attention probing for Vision Transformers! It’s almost as performant as full fine-tuning while being similarly compute efficient as standard linear probing and more interpretable! More in 🧵👇🏼
Preprint: arxiv.org/abs/2601.09322
arxiv.org Attentive multilayer fusion for vision transformers With the rise of large-scale foundation models, efficiently adapting them to downstream tasks remains a central challenge. Linear probing, which freezes the backbone and trains a lightweight head,...
Laure Ciernik @lciernik Jan 16
Why you should probe more than just the final layer of your Vision Transformer to maximize performance. 🧵👇
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Lukas Muttenthaler @lukas_mut
Even though I am not part of the Re-Align organizer team anymore, I am still cheering for this workshop! I still (and will always) believe that Re-Align is a great place for connecting the fields of AI / ML, Neuroscience, and Cognitive Science :) Can highly recommend submitting!
Ilia Sucholutsky Verified account @sucholutsky Jan 7
🥳 Come join us for the 4th installment of ReAlign, the best* workshop at ICLR! 🥳

You'll join our growing community of AI, cogsci, and neuro researchers studying representational alignment among natural and artificial minds.

(*Disclaimer: I might be a bit biased 😅)
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