Happy to share that we have 8 papers accepted to #ICML2026! 🎉 Most of the authors will be traveling to Seoul, Korea 🇰🇷 — feel free to reach out and chat with them if you’re interested in the work. See the full thread below for our papers 👇

Why you should probe more than just the final layer of your Vision Transformer to maximize performance. 🧵👇

Check out our new work on getting more out of your Vision Transformers without fine-tuning by leveraging intermediate representations when probing! We find that attentive probing is most robust for fusing across layers while showing exactly which layers matter for what task 👇

Laure Ciernik@lciernik.bsky.social · 7mo ago

Why you should probe more than just the final layer of your Vision Transformer to maximize performance. 🧵👇

Check out our new work on getting more out of your Vision Transformers without fine-tuning by leveraging intermediate representations when probing! We find that attentive probing is most robust for fusing across layers while showing exactly which layers matter for what task 👇

Laure Ciernik@lciernik.bsky.social · 7mo ago

Why you should probe more than just the final layer of your Vision Transformer to maximize performance. 🧵👇

3/ Noise Hypernetworks: Amortizing Test-Time Compute in Diffusion Models @lucaeyring.bsky.social , @shyamgopal.bsky.social , Alexey Dosovitskiy, @natanielruiz.bsky.social , @zeynepakata.bsky.social [Paper]: arxiv.org/abs/2508.09968 [Code]: github.com/ExplainableM...

🎓PhD Spotlight: Karsten Roth Celebrate @confusezius.bsky.social , who defended his PhD on June 24th summa cum laude! 🏁 His next stop: Google DeepMind in Zurich! Join us in celebrating Karsten's achievements and wishing him the best for his future endeavors! 🥳

From cell lines to full embryos, drug treatments to genetic perturbations, neuron engineering to virtual organoid screens — odds are there’s something in it for you! Built on flow matching, CellFlow can help guide your next phenotypic screen: biorxiv.org/content/10.1101/2025.04.11.648220v1

(4/4) Disentangled Representation Learning with the Gromov-Monge Gap @lucaeyring.bsky.social will present GMG, a novel regularizer that matches prior distributions with minimal geometric distortion. 📍 Hall 3 + Hall 2B #603 🕘 Sat Apr 26, 10:00 a.m.–12:30 p.m.

(3/4) Disentangled Representation Learning with the Gromov-Monge Gap A fantastic work contributed by Theo Uscidda and @lucaeyring.bsky.social , with @confusezius.bsky.social , @fabiantheis.bsky.social , @zeynepakata.bsky.social , and Marco Cuturi. 📖 [Paper]: arxiv.org/abs/2407.07829

Disentangled Representation Learning with the Gromov-Monge Gap

Learning disentangled representations from unlabelled data is a fundamental challenge in machine learning. Solving it may unlock other problems, such as generalization, interpretability, or fairness. ...

arxiv.org

Happy to share that we have 4 papers to be presented in the coming #ICLR2025 in the beautiful city of #Singapore . Check out our website for more details: eml-munich.de/publications. We will introduce the talented authors with their papers very soon, stay tuned😉

Thrilled to announce that four papers from our group have been accepted to #CVPR2025 in Nashville! 🎉 Congrats to all authors & collaborators. Our work spans multimodal pre-training, model merging, and more. 📄 Papers & codes: eml-munich.de#publications See threads for highlights in each paper. #CVPR

📄 Disentangled Representation Learning with the Gromov-Monge Gap with Théo Uscidda, Luca Eyring, @confusezius.bsky.social, Fabian J Theis, Marco Cuturi 📄 Decoupling Angles and Strength in Low-rank Adaptation with Massimo Bini, Leander Girrbach

Missing the deep learning part? go check out the follow up work @neuripsconf.bsky.social (tinyurl.com/yvf72kzf) and @iclr-conf.bsky.social (tinyurl.com/4vh8vuzk)

GENOT: Entropic (Gromov) Wasserstein Flow Matching with...

Single-cell genomics has significantly advanced our understanding of cellular behavior, catalyzing innovations in treatments and precision medicine. However, single-cell sequencing technologies are...

tinyurl.com

@dominik1klein.bsky.social · 2y ago

Good to see moscot-tools.org published in @nature.com ! We made existing Optimal Transport (OT) applications in single-cell genomics scalable and multimodal, added a novel spatiotemporal trajectory inference method and found exciting new biology in the pancreas! tinyurl.com/33zuwsep

Good to see moscot-tools.org published in @nature.com ! We made existing Optimal Transport (OT) applications in single-cell genomics scalable and multimodal, added a novel spatiotemporal trajectory inference method and found exciting new biology in the pancreas! tinyurl.com/33zuwsep

Mapping cells through time and space with moscot - Nature

Moscot is an optimal transport approach that overcomes current limitations of similar methods to enable multimodal, scalable and consistent single-cell analyses of datasets across spatial and temporal...

tinyurl.com

Today is a great day for optimal transport 🎉! Lots of gratitude 🙏 for all folks who contributed to ott-jax.readthedocs.io and pushed for the MOSCOT (now @ nature!) paper, from visionaries @dominik1klein.bsky.social, G. Palla, Z. Piran to the magician, Michal Klein! ❤️ www.nature.com/articles/s41...

Mapping cells through time and space with moscot - Nature

Moscot is an optimal transport approach that overcomes current limitations of similar methods to enable multimodal, scalable and consistent single-cell analyses of datasets across spatial and temporal...

nature.com

Helmholtz Munich@helmholtzmunich.bsky.social · 2y ago

AI in Cell Research: Unveiling Organ Development #Moscot, a groundbreaking #AI tool developed by an international team led by #HelmholtzMunich, tracks cells in real time, revealing how organs like the #pancreas form. t1p.de/viil5 @fabiantheis.bsky.social @nature.com

This is maybe my favorite thing I've seen out of #NeurIPS2024. Head over to HuggingFace and play with this thing. It's quite extraordinary.

Luca Eyring@lucaeyring.bsky.social · 2y ago

Thanks to @fffiloni.bsky.social and @natanielruiz.bsky.social, we have a running live Demo of ReNO, play around with it here: 🤗: huggingface.co/spaces/fffil... We are excited to present ReNO at #NeurIPS2024 this week! Join us tomorrow from 11am-2pm at East Exhibit Hall A-C #1504!

ReNO shows that some initial noise are better for some prompts! This is great to improve image generation, but i think it also shows a deeper property of diffusion models.

Luca Eyring@lucaeyring.bsky.social · 2y ago

Can we enhance the performance of T2I models without any fine-tuning? We show that with our ReNO, Reward-based Noise Optimization, one-step models consistently surpass the performance of all current open-source Text-to-Image models within the computational budget of 20-50 sec! #NeurIPS2024

Can we enhance the performance of T2I models without any fine-tuning? We show that with our ReNO, Reward-based Noise Optimization, one-step models consistently surpass the performance of all current open-source Text-to-Image models within the computational budget of 20-50 sec! #NeurIPS2024

After a break of over 2 years, I'm attending a conference again! Excited to attend NeurIPS, even more so to be presenting ReNO, getting inference-time scaling and preference optimization to work for text-to-image generation. Do reach out if you'd like to chat!