Post #847

GraphML News (June 15th) - ICML’24 graph papers, musings on AF3, more Flow Matching 🎉 ICML 2024 papers (including orals and spotlights) are now visible on OpenReview (however, without search). If you don’t want to scroll through 100 pages of accepted papers manually or write a custom parser, Azmine Toushik Wasi compiled a collection of accepted Graph ML papers with a nice categorization. 👨‍🔬 More blogs on AlphaFold 3 and reflexions about the future of TechBio: Charlie Harris focuses more on the technical side whereas Carlos Outeiral presents the CompBio perspective highlighting some cases where AF3 still underperforms. 🔀 Flow Matching continues to reach new heights with recently released papers: Variational Flow Matching (you didn’t forget ELBO and KL divergence, right?) by the UvA team of Floor Eijkelboom, Grigory Bartosh, et al (feat. Max Welling) derives a generalized flow matching formulation that naturally allows for categorical data (😼 CatFlow) and graph generation - the model outperform DiGress and other diffusion baselines. At the same time, the NYU team of Boffi et al propose Flow Map Matching - pretty much the Consistency Models for FMs that enable generation in one step instead of 20-100. Finally, Ross Irwin et al from AstraZeneca come up with MolFlow - flow matching for generating 3D conformations of molecules showing compelling results on QM9 and Geom-Drugs. 📚Weekend reading (no flow matching): GraphStorm: all-in-one graph machine learning framework for industry applications by Da Zheng and AWS - we wrote about a new GNN framework for enterprises back in 2023, here is the full paper with details. CRAG -- Comprehensive RAG Benchmark from Meta (and a Kaggle competition for $30k) - the factual QA benchmark that simulates queries to knowledge graphs and APIs. Vanilla RAG yields only 44% accuracy and fancy industrial models barely reach 63% - so a plenty of room for improvements. Explainable Graph Neural Networks Under Fire - by Zhong Li feat Stephan Günnemann. Turns out most GNN explainers utterly fail and cannot be trusted in the presence of simple adversarial perturbations. Let us know if you ever found a successful working case for GNN explainers 🤭