Graph ML News (Aug 18th)

A new blog post Designing Deep Networks to Process Other Deep Networks by Haggai Maron, Ethan Fetaya, Aviv Navon, Aviv Shamsian, Idan Achituve, and Gal Chechik applies the concepts of symmetry and invariances (common tools in Geometric DL) to the task of predicting model weights. Working in the Deep Weight Space (all parameters of neural networks), we want neural architectures to be invariant to permutations of neurons because mathematically any permutation should still encode the same function.

Two papers appeared almost simultaneously, PoseBusters by Buttenschoen et al and PoseCheck by Harris et al, providing a critical look on modern generative models (often diffusion-based) for protein-ligand docking and structure-based drug design. PoseBusters finds that generative models often have problems with physical plausibility of the generated outputs while PoseCheck finds many nonphysical features in generated molecules and poses. Huge opportunities for improving equivariant diffusion models!

The Simons Institute for the Theory of Computing held a workshop on large language models and transformers. It was not very much into graph learning but still featured a handful of talks on core topics that will be in graph ML sooner or later. Featuring talks by Chris Manning, Yejin Choi, Ilya Sutskever, Sasha Rush, and other famous researchers — the playlist with recorded talks is already on YouTube 👀

Weekend reading:

Score-based Enhanced Sampling for Protein Molecular Dynamics feat. Jian Tang - a score-based model for approximating MD calculations.