Post #807

GraphML News (Nov 25th) - LOG’23 starts next week, EEML’24, The OpenAI drama has been successfully resolved and now the ML world is wandering about Q* that (allegedly) showed some amazing improvements towards AGI. Henceforth, Q-learning and classic A* search algorithm are among the hottest trends this week 🤣 (where I could insert a shameless plug and point that audience to the neural A*Net paper we’ll be presenting at NeurIPS’23). Besides that: LoG 2023, the graph’iest ML conference, starts next Monday, November 27th — registration is free and participation is fully-remote. It would have been nicer to have a list of accepted papers and tutorials a bit before the very starting day of the conference 🙂 (perhaps the organizers are also busy with ICLR rebuttals and NeurIPS workshops). The Eastern European ML Summer School (EEML) has just announced its 2024 installment (15-20 July, Novi Sad, Serbia) featuring a stellar lineup of speakers and organizers including Kyunghyun Cho, Doina Precup, Michael Bronstein, Alfredo Canziani, Petar Veličković, and many more prominent researchers (especially from DeepMind). So we would expect quite a few lectures and tutorials on graph learning! Weekend reading: Generalized Biomolecular Modeling and Design with RoseTTAFold All-Atom from the Baker Lab - all-atom versions of RosettaFold (RFAA) for structure prediction and RFdiffusionAA for protein-ligand binding generative model. Geometric Algebra Transformer (GATr 🐊) by Johann Brehmer feat. Taco Cohen. GATr = Clifford Algebras + Transformers, built-in E(3) equivariance. Some applications include n-body dynamics, wall-shear-stress estimation of human arteries, and robotic planning. The code was recently published as well. A Survey of Graph Meets Large Language Model: Progress and Future Directions by Yuhan Li et al. The subfield exists for a few months but there is already a survey about it.