Post #826

GraphML News (Feb 17th) - PyG 2.5, VantAI deal, Discrete Flow Matching, Position papers Sora and Gemini 1.5 took all the ML news feeds this week - let’s check what is there in graph learning beyond the main wave of AI anxiety and stress for grad students. 🔥 A fresh release PyG 2.5 features a new distributed training framework (co-authored by Intel engineers), RecSys support with easy retrieval techniques like MIPS over node embeddings, new Edge Index representation instead of sparse tensors, and rewritten Message Passing class for torch.compile. Lots of new cool stuff! 📚 Xavier Bresson (NUS Singapore) started publishing the slides and notebooks of his most recent 22/23 GraphML course - highly recommended to check it out. Hopefully, this initiative would encourage folks running Graph & Geometric DL courses at Oxbrige to publish their lectures as well 😉 💸 The $674M (in biobucks) deal was announced between VantAI and Bristol Myers Squibb for developing molecular glues. Besides publishing on generative models, VantAI runs open seminars on GenAI for drug discovery (the most recent talk on FoldFlow is already on YouTube). 📐 Two papers from the MIT team of Regina Barzilay and Tommi Jaakkola introduce flow matching for discrete variables (like atom types or DNA base pairs): Dirichlet Flow Matching with Applications to DNA Sequence Design by Hannes Stärk, Bowen Jing, feat. Gabriele Corso - by defining flows on a simplex where the prior is a uniform Dirichlet distribution. Also supports classifier-free guidance and Consistency models-like distillation to perform generation in one forward pass. Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design by Andrew Campbell, Jason Yim, et al - by using Continuous Time Markov Chains (CTMC) where the prior distribution is either a uniform or all-mask absorbed state (similar to discrete diffusion models). The resulting Multiflow model now has all necessary components of protein backbone generation implemented as flow matching (translation and rotation as continuous FM, and amino acids as discrete FM). Position papers for the weekend reading: Future Directions in Foundations of Graph Machine Learning by Chris Morris feat. Haggai Maron, Michael Bronstein, Stefanie Jegelka and others - on expressive power, generalization, and optimization of GNNs. Position Paper: Challenges and Opportunities in Topological Deep Learning by Theodore Papamarkou feat. Bastian Rieck, Michael Schaub, Petar Veličković and a huge authors team - on theoretical and practical challenges of TDL. Graph Foundation Models by Haitao Mao feat. Neil Shah, Michael Galkin, and Jilian Tang - finally, a non-LLM discussion on designing foundation models on graphs and for all kinds of graph tasks. The authors hypothesize what could be the transferable and invariant graph vocabulary given heterogeneity of graph structures and their features spaces, and how Graph FMs might benefit from scaling laws (namely, what should be scaled and where it doesn’t bring benefits)