Post #715

Hot New Graph ML Submissions from ICLR 🧬 Diffusion remains the top trend in AI/ML venues this year, including the graph domain. Ben Blaiszik compiled a Twitter thread of interesting papers in AI 4 Science domain including material discovery, catalyst discovery, and crystallography. Particularly cool works: - Protein structure generation via folding diffusion by the collab between Stanford and MSR - Kevin E. Wu, Kevin K. Yang, Rianne van den Berg, James Y. Zou, Alex X. Lu, Ava P. Amini - why do you need AlphaFold and MSAs if you can just train a diffusion model to predict all the structure? 😉 - Dynamic-Backbone Protein-Ligand Structure Prediction with Multiscale Generative Diffusion Models by NVIDIA and Caltech - Zhuoran Qiao, Weili Nie, Arash Vahdat, Thomas F. Miller III, Anima Anandkumar - DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking by MIT - Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, Tommi Jaakkola - the next version of the famous EquiDock and EquiBind combined with the recent Torsional Diffusion. - We’d include here a novel benchmark work Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design by Stanford and University of Toronto - AkshatKumar Nigam, Robert Pollice, Gary Tom, Kjell Jorner, Luca A. Thiede, Anshul Kundaje, Alan Aspuru-Guzik 📚 In a more general context, Yilun Xu shared a Google Sheet with ICLR submissions on diffusion papers and score-based generative modeling including trendy text-to-video models announced by FAIR and Google. 🤖 Derek Lim compiled a Twitter thread on 10+ ICLR submissions on Graph Transformers - the field looks a bit saturated at the moment, let’s see what reviewers say. 🪓 Michael Bronstein’s lab at Twitter announced two cool papers: - Gradient Gating for Deep Multi-Rate Learning on Graphs by the collab between ETH Zurich, Oxford, and Berkley - T. Konstantin Rusch, Benjamin P. Chamberlain, Michael W. Mahoney, Michael M. Bronstein, Siddhartha Mishra. A clever trick improving a standard residual connection to allow nodes to get updated ad different speeds. A blast from the past - GraphSAGE from 2017 with gradient gating becomes a unanimous leader by a large margin in heterophilic graphs 👀 - Graph Neural Networks for Link Prediction with Subgraph Sketching by Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi, Fabrizio Frasca, Thomas Markovich, Nils Hammerla, Michael M. Bronstein, Max Hansmire. A neat usage of sketching to encode subgraphs in ELPH and its more scalable buddy BUDDY for solving link prediction in large graphs.