(1/n)🚨Train a model solving DFT for any geometry with almost no training data
Introducing Self-Refining Training for Amortized DFT: a variational method that predicts ground-state solutions across geometries and generates its own training data!
📜 arxiv.org/abs/2506.01225
💻 github.com/majhas/self-...

David is disrupting everone's neurips grind by putting out amazing works, such a dirty trick!

Renormalisation is a central concept in modern physics. It describes how the dynamics of a system change at different scales. A great way to understand and visualise renormalisation is the Ising model

(some math, but one can follow without it )

1/13

SuperDiff goes super big!
- Spotlight at #ICLR2025!🥳
- Stable Diffusion XL pipeline on HuggingFace huggingface.co/superdiff/su... made by Viktor Ohanesian
- New results for molecules in the camera-ready arxiv.org/abs/2412.17762
Let's celebrate with a prompt guessing game in the thread👇

We've been sharing these projects during the year, but today, they have been accepted at #ICLR2025 (1-3) and #AISTATS2025 (4)

🧵(1/7) Have you ever wanted to combine different pre-trained diffusion models but don't have time or data to retrain a new, bigger model?

🚀 Introducing SuperDiff 🦹‍♀️ – a principled method for efficiently combining multiple pre-trained diffusion models solely during inference!

10 minutes ago

I am excited to share a perspective on the much-needed topic of hashtag#safety for hashtag#selfdrivinglaboratories. As the field progresses, understanding the challenges and gaps in building safe setups will be crucial for scaling up this technology!

doi.org/10.26434/che...

With some delay, JetFormer's *prequel* paper is finally out on arXiv: a radically simple ViT-based normalizing flow (NF) model that achieves SOTA results in its class.

Jet is one of the key components of JetFormer, deserving a standalone report. Let's unpack: 🧵⬇️

Come join us in Singapore at #ICLR2025 to discuss the latest developments everywhere where Learning meets Sampling!

We're presenting our spotlight paper on transition path sampling at #NeurIPS2024 this week! Learn how to speed up the conventional Monte Carlo approaches by orders of magnitude

Wed 11 Dec 4:30 pm #2606
arxiv.org/abs/2410.07974

first authors = {Yuanqi Du, Michael Plainer, @brekelmaniac.bsky.social}

so happy to see that Action Matching finds its applications in physics, outperforming diffusion models and Flow Matching!

wonderful work by Jules Berman, Tobias Blickhan, and Benjamin Peherstorfer!
arxiv.org/abs/2410.12000