Post #886

GraphML News (Feb 15th) - ICLR 2025 papers, Upcoming Workshops, Latent Labs 📚 All ICLR 2025 accepted papers and their respective categories (orals, spotlights, posters) are now visible on OpenReview (as well as rejected papers) - we’ll make an (opinionated) list with the most interesting works. A plenty of weekend reading meanwhile. 🏁 A few announcements and upcoming deadlines: the Helmholtz-ELLIS Workshop on Foundation Models in Science will take place on March 18-19 in Berlin, speakers include a nice mix of AI 4 Science researchers like Tian Xie (MSR AI 4 Science), Shirley Ho (Flatiron), as well as hardcore LLMers like Michal Valko (Meta), Tim Dettmers (AI2), and many others. The application deadline for the LOGML Summer School 2025 (London Geometry and ML) is February 16th. The summer school will take place July 7-11 in London. 💸 Latent Labs, a startup in generative protein design, came out of stealth with $50M funding from Radical Ventures, Sofinnova Partners, Jeff Dean, and Aidan Gomez. Latent Labs is founded largely by ex-DeepMind researchers who worked on AlphaFold 2 and 3, so the technical expertise is definitely there. We’ll keep an eye on their progress! What else to read (other than your 10 ICML papers to review): Spectral Journey: How Transformers Predict the Shortest Path by Andrew Cohen and Meta AI - turns out that a 2-layer transformer, when asked to find the shortest path on a graph, computes a Laplacian of the line graph and selects the next edge based on its distance to the target in the latent space. MDCrow: Automating Molecular Dynamics Workflows with Large Language Models by Quintina Campbell et al feat. Andrew White - a new crow in the aviary - an LLM agent that can perform molecular dynamics with OpenMM, you can query it with questions like “Simulate protein 1ZNI at 300 K for 0.1 ps and calculate the RMSD over time.” and generate analytical charts. The code is available. Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics by Sebastian Sanokowski and JKU Linz - having tried discrete diffusion for combinatorial optimization myself, I could second that it’s hard to make it work. This paper makes the application of diffusion models in CO much easier.