34 citations · 71 across the 8 of their papers we have counts for
9 papers
What Does It Take to Be a Good AI Research Agent? Studying the Role of Ideation Diversity
Alexis Audran-Reiss, Jordi Armengol-Estapé, Karen Hambardzumyan +17
AI research agents offer the promise to accelerate scientific progress by automating the design, implementation, and training of machine learning models. However, the field is stil…
UMA: A Family of Universal Models for Atoms
Brandon M. Wood, Misko Dzamba, Xiang Fu +15
The ability to quickly and accurately compute properties from atomic simulations is critical for advancing a large number of applications in chemistry and materials science includi…
Does equivariance matter at scale?
Johann Brehmer, Sönke Behrends, Pim de Haan +1
Given large datasets and sufficient compute, is it beneficial to design neural architectures for the structure and symmetries of each problem? Or is it more efficient to learn them…
Information-driven Affordance Discovery for Efficient Robotic Manipulation
Pietro Mazzaglia, Taco Cohen, Daniel Dijkman
Robotic affordances, providing information about what actions can be taken in a given situation, can aid robotic manipulation. However, learning about affordances requires expensiv…
FoMo Rewards: Can we cast foundation models as reward functions?
Ekdeep Singh Lubana, Johann Brehmer, Pim de Haan +1
We explore the viability of casting foundation models as generic reward functions for reinforcement learning. To this end, we propose a simple pipeline that interfaces an off-the-s…
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems
Alexandre Duval, Simon V. Mathis, Chaitanya K. Joshi +7
Recent advances in computational modelling of atomic systems, spanning molecules, proteins, and materials, represent them as geometric graphs with atoms embedded as nodes in 3D Euc…
Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers
Pim de Haan, Taco Cohen, Johann Brehmer
The Geometric Algebra Transformer (GATr) is a versatile architecture for geometric deep learning based on projective geometric algebra. We generalize this architecture into a bluep…
Information-driven Affordance Discovery for Efficient Robotic Manipulation
Pietro Mazzaglia, Taco Cohen, Daniel Dijkman
Robotic affordances, providing information about what actions can be taken in a given situation, can aid robotic manipulation. However, learning about affordances requires expensiv…
Natural Graph Networks
Pim de Haan, Taco Cohen, Max Welling
A key requirement for graph neural networks is that they must process a graph in a way that does not depend on how the graph is described. Traditionally this has been taken to mean…