229 citations · 553 across the 32 of their papers we have counts for
7 papers · 1 filter
Improving Compositional Generalization Using Iterated Learning and Simplicial Embeddings
Yi Ren, Samuel Lavoie, Mikhail Galkin +2
Compositional generalization, the ability of an agent to generalize to unseen combinations of latent factors, is easy for humans but hard for deep neural networks. A line of resear…
Towards Foundation Models for Materials Science: The Open MatSci ML Toolkit
Kin Long Kelvin Lee, Carmelo Gonzales, Matthew Spellings +3
Artificial intelligence and machine learning have shown great promise in their ability to accelerate novel materials discovery. As researchers and domain scientists seek to unify a…
Towards Foundation Models for Knowledge Graph Reasoning
Mikhail Galkin, Xinyu Yuan, Hesham Mostafa +2
Foundation models in language and vision have the ability to run inference on any textual and visual inputs thanks to the transferable representations such as a vocabulary of token…
MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling
Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar +3
We propose MatSci ML, a novel benchmark for modeling MATerials SCIence using Machine Learning (MatSci ML) methods focused on solid-state materials with periodic crystal structures.…
Approximate Answering of Graph Queries
Michael Cochez, Dimitrios Alivanistos, Erik Arakelyan +6
Knowledge graphs (KGs) are inherently incomplete because of incomplete world knowledge and bias in what is the input to the KG. Additionally, world knowledge constantly expands and…
Neural Graph Reasoning: Complex Logical Query Answering Meets Graph Databases
Hongyu Ren, Mikhail Galkin, Michael Cochez +2
Complex logical query answering (CLQA) is a recently emerged task of graph machine learning that goes beyond simple one-hop link prediction and solves a far more complex task of mu…
Attending to Graph Transformers
Luis Müller, Mikhail Galkin, Christopher Morris +1
Recently, transformer architectures for graphs emerged as an alternative to established techniques for machine learning with graphs, such as (message-passing) graph neural networks…