Really proud of this project led by @kylecranmer.bsky.social and created alongside these awesome collaborators and friends. I believe this is important work that will help researchers make grounded decisions when building multimodal models with many diverse inputs. Add it to your paper pile!

Does a smaller latent space lead to worse generation in latent diffusion models? Not necessarily! We show that LDMs are extremely robust to a wide range of compression rates (10-1000x) in the context of physics emulation. We got lost in latent space. Join us 👇

Call for Papers is out for the NeurIPS 2025 Workshop on ML & the Physical Sciences! Submit your 4-page papers on new directions in ML & physics by Friday, August 29th 🌌 💻 ⚛️ #ML4PS #ML4PS2025 ml4physicalsciences.github.io

ouroborosly

Great talk by @annalenakofler.bsky.social Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics ml4physicalsciences.github.io/2024/files/N...

A wide range of insightful and inspiring talks today at #ML4PS @neuripsconf.bsky.social , including @yasamanbb.bsky.social , Thea Aarrestad, and @annalenakofler.bsky.social

Some highlights from a really compelling overview talk by @lukasheinrich.com this morning at #ML4PS at @neuripsconf.bsky.social -- reflecting on the future of ML for physics, including the importance of multi-modality and when/how to impose our scientific intuitions onto our models.

Really looking forward to these!

🧵 Today with @polymathicai.bsky.social and others we're releasing two massive datasets that span dozens of fields - from bacterial growth to supernova! We want this to enable multi-disciplinary foundation model research.