Post #876

GraphML News (Nov 30th) - LOG recordings, The Chip Has Sailed, Illustrated Flow Matching 📺 LOG 2024 has just wrapped up, and all recordings are now available on the official YouTube channel, featuring: - Keynotes from Yusu Wang (UCSD), Zach Ulissi (Meta), Xavier Bresson (NUS), Alden Hung (Isomorphic Labs) - Tutorials on geometric generative models, neural algorithmic reasoning, GNNs for time series, heterophilic graph learning, and KGs + LLMs - All oral presentations A lot of stuff to digest over the weekend if you missed it! ⛵️ The GNNs for chip design saga continues: - the seminal 2021 Nature paper (now known as AlphaChip) spun off some controversy from the chip design community about reproducibility (however, other people say it’s rather a skill issue and the CD community just doesn’t have proper deep learning chops), - several rounds of message exchange led to the official Addendum on the Nature paper clarifying the concerns and adding that pre-training is a must. - In Oct 2024, Igor Markov (Synopsys) published an article at ACM Communications with a further criticism of the approach. - Recently, in Nov 2024, the lead authors of AlphaChip (Anna Goldie, Azalia Mirhoseini, and Jeff Dean himself) recently put an arxiv paper The Chip Has Sailed with the full timeline and emphasizing that the approach is actually already working within Google and AlphaChip has been used in several generations of chips used in production. Perhaps one of the most important messages from this paper is that while academics and industry debate whether the approach could work, it is actually already working. We’ll keep you posted! 🖌️A Visual Dive into Conditional Flow Matching by Anne Gagneux, Ségolène Martin, Rémi Emonet, Quentin Bertrand, and Mathurin Massias to flow matching is what the Illustrated Transformer to transformers - a detailed visual guide on the inner workings of flow matching and math behind it, a highly recommended reading. 🪠PLUMBER from Bioptic is the new protein-ligand benchmark of 1.8M data points based on PLINDER and enriched with more data from BindingDB, ChEMBL, and BioLip 2 to probe robustness of protein-ligand binding models in more diverse compositional generalization tests.