Luca Eyring

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 👇

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 👇

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.

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. ...

(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

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)

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...

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

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...

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.

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.

GitHub - ExplainableML/ReNO: [NeurIPS 2024] ReNO: Enhancing One-step Text-to-Image Models through Reward-based Noise Optimization [NeurIPS 2024] ReNO: Enhancing One-step Text-to-Image Models through Reward-based Noise Optimization - ExplainableML/ReNO

This is joint work with @shyamgopal.bsky.social (co-lead), @confusezius.bsky.social, Alexey, and @zeynepakata.bsky.social.

To dive into all the details, please check out:

Code: github.com/ExplainableM...
Paper (updated with latest FLUX-Schnell + ReNO results): arxiv.org/abs/2406.043...

Even within the same computational budget, a ReNO-optimized one-step model outperforms popular multi-step models such as SDXL and PixArt-α. Additionally, our strongest model, ReNO-enhanced HyperSDXL, is on par even with SOTA proprietary models, achieving a win rate of 54% vs SD3.

ReNO optimizes the initial noise in one-step T2I models at inference based on human preference reward models. We show that ReNO achieves significant improvements over five different one-step models quantitatively on common benchmarks and using comprehensive user studies.

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!

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!