GraphML News (Nov 11th) - MoML, GFlowNets workshop, NeurIPS workshops and blogs

👺 Brace yourselves, ICLR reviews are out and Reviewer 2 is most dangerous in those wildest conditions.

🧬 The Molecular ML conference at MIT happened a few days ago and brought together folks from geometric DL, computational biology, drug discovery, protein learning, and materials science. We won’t have the recordings, but the list of accepted posters is published and you can find many of them on arxiv already.

🌊 A handful of the MoML posters featured Generative Flow Networks (GFlowNets), and its authors at Mila organized a whole 3-day workshop on the basics of generative modeling and foundations of GFlowNets with many practical examples - all 24 hours of streams are available on YouTube now.

More NeurIPS workshops opened the lists of accepted papers: AI for Accelerated Materials Design, Temporal Graph Learning, Mathematical Reasoning and AI

✍️ And several new blogposts have arrived:

- Equivariant neural networks  –  *what*, *why* and *how* ? by Maurice Weiler - Part 1 out of the planned five chapters explaining the main ideas in the recent book on Equivariant and Coordinate Independent CNNs.
- ULTRA: Foundation Models for Knowledge Graph Reasoning by our small team featuring the invited guest Bruno Ribeiro (Purdue) where we give more visual explanation on the motivation and mechanisms behind our recent paper on KG reasoning.

Weekend reading:

From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction by Nima Shoghi feat. Larry Zitnick (Meta AI and CMU) - pretty much a foundation model for many molecular and materials science tasks - a pre-trained 230M params GemNet-OC, lots of engineering insights on training such complex models on diverse and imbalanced datasets

Examining graph neural networks for crystal structures: Limitations and opportunities for capturing periodicity by Sheng Gong feat. Tian Xie, Rafael Gomez-Bombarelli, Shuiwang Ji - on the eternal quest of designing abstractions for periodic structures where GNNs can still fail

Efficient Subgraph GNNs by Learning Effective Selection Policies by Beatrice Bevilacqua feat. Bruno Ribeiro, Haggai Maron - on improving notoriously hungry subgraph GNNs with Gumbel-Softmax and Straight-through estimation tricks

Locality-Aware Graph-Rewiring in GNNs (NeurIPS’23) by Federico Barbero feat Michael Bronstein and Francesco Di Giovanni - introduces the LASER graph rewiring method that produces a sequence of rewired graph snapshots with new edges selected based on the connectivity and locality constraints. Fast and effective!