Post #764

Graph ML News, April 1st edition Apart from Neural Graph Databases and Twitter Algorithm (and SIGBOVIK), a few more things happened this week. The Learning on Graphs Conference (LoG) 2023 has been announced! One of the most premiere graph learning venues is going to take place online on Nov 27-30th accompanied by local meetups, you can actually volunteer and organize it at your place! Baker Lab open-sourced RF Diffusion, a SOTA protein generation model, as part of ColabFold. We covered RF Diffusion a few months ago and its capabilities are quite astounding. Since the time of announcement, the authors further improved the quality and managed to test hundreds of generated proteins in the wet lab to test their properties. ICML 2023 announced accepted workshops - the graph learning audience might want to attend: - Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators - Topology, Algebra, and Geometry in Machine Learning (TAG-ML) - Knowledge and Logical Reasoning in the Era of Data-driven Learning - Sampling and Optimization in Discrete Space - The Synergy of Scientific and Machine Learning Modelling (SynS & ML) - Workshop on Computational Biology - Structured Probabilistic Inference and Generative Modeling Rishi Puri and Matthias Fey published a post on accelerating Heterogeneous Graph Transformers in pyg-lib resulting in about 3x speed boost. Meanwhile, AWS Labs released GraphStorm, a Graph ML framework for enterprise use-cases based on DGL. For the weekend reading, check out Machine Learning for Partial Differential Equations by Steven L. Brunton and J. Nathan Kutz - perhaps the best intro into ML with PDEs. Yes, it is from the author of awesome YouTube lectures on dynamical systems, physics-inspired ML, and control theory.