124 citations
- Université de MontréalCA24 papers
- McGill UniversityCA12 papers
- Flatiron Health (United States)US8 papers
- Flatiron Institute7 papers
- Centre for Research in Astrophysics of QuébecCA4 papers
- Polytechnique MontréalCA4 papers
- University of TorontoCA4 papers
- CentraleSupélecFR3 papers
- Centre National de la Recherche ScientifiqueFR3 papers
- New York University Abu DhabiAE3 papers
- Queen's UniversityCA3 papers
- Université Paris-SaclayFR3 papers
10 papers · 1 filter
Evaluating representation learning on the protein structure universe
Arian R. Jamasb, Alex Morehead, Chaitanya K. Joshi +8
We introduce ProteinWorkshop, a comprehensive benchmark suite for representation learning on protein structures with Geometric Graph Neural Networks. We consider large-scale pre-tr…
On the importance of catalyst-adsorbate 3D interactions for relaxed energy predictions
Alvaro Carbonero, Alexandre Duval, Victor Schmidt +4
The use of machine learning for material property prediction and discovery has traditionally centered on graph neural networks that incorporate the geometric configuration of all a…
Stochastic Average Gradient : A Simple Empirical Investigation
Pascal Junior Tikeng Notsawo
Despite the recent growth of theoretical studies and empirical successes of neural networks, gradient backpropagation is still the most widely used algorithm for training such netw…
Inferring dynamic regulatory interaction graphs from time series data with perturbations
Dhananjay Bhaskar, Sumner Magruder, Edward De Brouwer +4
Complex systems are characterized by intricate interactions between entities that evolve dynamically over time. Accurate inference of these dynamic relationships is crucial for und…
Can Forward Gradient Match Backpropagation?
Louis Fournier, Stéphane Rivaud, Eugene Belilovsky +2
Forward Gradients - the idea of using directional derivatives in forward differentiation mode - have recently been shown to be utilizable for neural network training while avoiding…
FAENet: Frame Averaging Equivariant GNN for Materials Modeling
Alexandre Duval, Victor Schmidt, Alex Hernandez Garcia +4
Applications of machine learning techniques for materials modeling typically involve functions known to be equivariant or invariant to specific symmetries. While graph neural netwo…