ICLR 2025 submissions are now available on OpenReview, here are some fresh GNNs and Geometric learning subs that caught my attention (and haven't appeared during ICML/NeurIPS cycles). Based on the abstracts, but PDFs should be there shortly 🧵 Thread! 1/n

BREAKING NEWS The Royal Swedish Academy of Sciences has decided to award the 2024 #NobelPrize in Chemistry with one half to David Baker “for computational protein design” and the other half jointly to Demis Hassabis and John M. Jumper “for protein structure prediction.”

I and Yaron Yaron Lipman are hiring PhD research interns for 2025 in New York City, to work on developing core foundational methods for generative modeling at scale. If you're familiar with some of our works, shoot us an email: {rtqichen,ylipman}@meta.com metacareers.com/jobs/532549086…

Excited to share our latest work on discrete diffusion models and the DDPP objective. A new approach for simulation-free fine-tuning. Check it out if you’re interested!

📢Annoucing EDLM, our brand-new Energy-based Language Model embedded with Diffusion framework! Key results: 1. We (for the first time?) almost match AR perplexity. 2. Significantly improved generation quality. 3. Considerable sampling speedup without quality drop. 🧵1/n

Does equivariance matter at scale? ... When the twitter discourse gets so tiring that you actually go out and collect EVIDENCE :D There has been a lot of discussion over the years about whether one should build symmetries into your architecture to get better data efficiency, or

🎉 Introducing Protenix! Our open-source AF3 reproduction offers the tools to train and explore protein folding models. Start here: github.com/bytedance/Prot… 🌟🌟 #ProteinFolding #OpenScience #AI4Biology

New article: "The geometry of data: the missing metric tensor and the Stein score" (blog.christianperone.com/2024/11/the-ge…). I show how you can derive a (efficient to compute) data manifold metric tensor with the Stein score alone ! Deep connections to diffusion, score-based models and physics.

Next gen alphafold, future generative models, holy-grail models for drug design, and agents for actual science. I’m looking for builders to join our incredibly talented ML scientists and engineers at Isomorphic Labs to create the future of drug design job-boards.greenhouse.io/isomorphiclabs…

UNLOCKING THE POWER OF GRADIENT GUIDANCE FOR STRUCTURE-BASED MOLECULE OPTIMIZATION • MolJO introduces the first gradient-based framework for structure-based molecule optimization (SBMO), offering joint optimization across continuous and discrete molecular properties. This

Announcing the NeurIPS 2024 Test of Time Paper Awards: blog.neurips.cc/2024/11/27/ann…

🎉Excited to announce our crystal generation model CrysBFN is accepted to #ICLR2025 as Spotlight paper! CrysBFN achieves ~100x speedup compared to diffusion based model with even better sampling quality! 😍 Project website: wu-han-lin.github.io/iclr25crysbfn/ Paper: arxiv.org/pdf/2502.02016

Can machine learning models predict their own errors 🤯 ? In a new preprint w/ Apple collaborators Aravind Gollakota, Parikshit Gopalan, Charlotte Peale, and Udi Wieder, we present a theory of loss prediction and show an equivalence with algorithmic fairness! A thread (1/n):

Excited to share my #ICLR2025 paper, with JC Hütter and friends! Genetic perturbation screens allow biologists to manipulate and measure the genes in cells = discover causal relationships! BUT they are expensive to run, expensive to interpret. ... We use LLMs to help!

Great Work！ Congratulations, Qiying！

🚀 Excited to release a major update to the Boltz-1 model: Boltz-1x! Boltz-1x introduces inference-time steering for much higher physical quality, CUDA kernels for faster, more memory-efficient inference and training, and more! 🔥🧵