Announcing Ambient Diffusion Omni — a framework that uses synthetic, low-quality, and out-of-distribution data to improve diffusion models. State-of-the-art ImageNet performance. A strong text-to-image results in just 2 days on 8 GPUs. Filtering ❌ Clever data use ✅

Ever wonder why switching optimizers never actually helps? Introducing HeavyBall Benchmark

This is not engagement bait. I guarantee that the answer is not what you expect.

As always, a very nice talk from Francois Lanusse: Generative Al for Inverse Problems and Forecasting Slides: eiffl.github.io/talks/Paris2025

Announcing Ambient Protein Diffusion, a state-of-the-art 17M-params generative model for protein structures. Diversity improves by 91% and designability by 26% over previous 200M SOTA model for long proteins. The trick? Treat low pLDDT AlphaFold predictions as low-quality data

🎉 Great news: Our Machine Learning and Physical Sciences workshop at NeurIPS Conference will be back again this year! 🎉 Keep an eye out for updates on deadlines etc, we will be updating the website soon ml4physicalsciences.github.io #ML4PS2025 Machine Learning And The Physical Sciences

#NeurIPS2025 removed the global rebuttal option?!? This was super useful (for authors AND reviewers AND area chairs AND readers) to get a summary of the common concerns of the reviewers and how the authors addressed them...

Not sure if the physics community is aware, but Polymathic AI has been quietly putting 10TB of physics simulation data on Hugging Face. ⛓️‍💥huggingface.co/collections/po…

We are excited to bring #EurIPS 2025 to Copenhagen in December. Consider becoming a sponsor and support us in making this event a success! Sponsorship packages are available and can be customized if necessary. Reach out if you have any questions Info: eurips.cc/become-sponsor/