Graph ML News (Sep 2nd) - TpuGraphs Kaggle competition, EvolutionaryScale

Google launched a proper graph learning Kaggle competition ”Fast or Slow?” with a $50k prize pool. The challenge is based off a recently released TpuGraphs dataset — given a computational graph (as a DAG), predict its runtime given a certain input configuration (on node- or graph-level) and get the fastest config. Practically, it can be framed as a regression or ranking problem. TpuGraphs is pretty large: 7k nodes / 31M configuration pairs for the layout collection, and 40 nodes / 13M pairs for the tile collection. Baselines include GCN and GraphSAGE, but we can probably expect Kaggle grandmasters to come up with creative gradient boosting and decision trees techniques as well 😉 So XGBoost or GNNs? The challenge is open until Nov 17th.

A few weeks ago we found out that Meta disbanded the protein team working on ESM, ESMFold, and a handful of other projects. Now we know that the ESM team formed EvolutionaryScale and raised about $40M of funding promising new versions of ESM every year. Great news for thousands of protein projects using ESM models!

Weekend reading:

TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs

Exploring "dark matter" protein folds using deep learning feat. Andreas Loukas, Michael Bronstein, and Bruno Correia