GraphML News (Sep 23rd) - Stanford Graph Learning Workshop, AlphaMisuse, PEFT for ESM

NeurIPS decisions for both tracks are out - congrats to those who made it in and encouragements to those who did not, hopefully the next iteration would get better! Our team got 2 papers accepted including A*Net - a scalable knowledge graph reasoning method that can be used, eg, for improving factual correctness of language models (demo is on github). Next weeks we can expect more accepted papers to be publicly available, so we’ll keep you updated. Don’t forget about the NeurIPS graph workshops many of which extended their deadlines to early October!

Stanford Graph Learning Workshop was officially announced and will take place physically on Oct 24th. This time the organizers published a call for contributed talks from the academic and industry tracks. I will try to be there, ping me if you want to chat.

Google DeepMind announced AlphaMisuse, a model for categorizing “missense” genetic mutations based on AlphaFold. AlphaMisuse predicted labels for ~60M possible missense mutations whereas humans covered at most ~700K. Unfortunately, the authors say the model weights won’t be released so let’s hope for re-implementations in open source ecosystems.

If you have been living under the rock, parameter-efficient fine-tuning (PEFT) techniques took the world of LLMs by the storm and it’s pretty much everywhere now. Amelie Schreiber wrote a great blogpost on applying LoRA to the ESM-2 family of protein LMs so even the beefiest of ESMs (still pretty small compared to Llama’s though) can be now fine-tuned on commodity GPUs. To learn more about PEFT, check out this fresh survey by Vladislav Lialin et al.

Some freshly accepted NeurIPS papers for the weekend reading:

Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics

SE(3) Equivariant Augmented Coupling Flows

When Do Graph Neural Networks Help with Node Classification: Investigating the Homophily Principle on Node Distinguishability

Fine-grained Expressivity of Graph Neural Networks

Next week ICLR’24 submissions become available, so oh boy we’ll have the weekend reading 👀