5 papers
Training a neural network to rapidly identify candidate gravitational-wave events in the lower mass gap
Nayyer Raza, Man Leong Chan, Daryl Haggard +5
The physics governing the boundary between the most massive neutron stars (NSs) and the least massive black holes (BHs) is currently uncertain, but could potentially be constrained…
Signal from Structure: Exploiting Submodular Upper Bounds in Generative Flow Networks
Alexandre Larouche, Audrey Durand
Generative Flow Networks (GFlowNets; GFNs) are a class of generative models that learn to sample compositional objects proportional to their a priori unknown value, their reward. W…
A Guide to Robust Generalization: The Impact of Architecture, Pre-training, and Optimization Strategy
Maxime Heuillet, Rishika Bhagwatkar, Jonas Ngnawé +6
Deep learning models operating in the image domain are vulnerable to small input perturbations. For years, robustness to such perturbations was pursued by training models from scra…
GWSkyNet-Multi II: an updated machine learning model for rapid classification of gravitational-wave events
Nayyer Raza, Man Leong Chan, Daryl Haggard +5
Multi-messenger observations of gravitational waves and electromagnetic emission from compact object mergers offer unique insights into the structure of neutron stars, the formatio…
Neural Bandits for Data Mining: Searching for Dangerous Polypharmacy
Alexandre Larouche, Audrey Durand, Richard Khoury +1
Polypharmacy, most often defined as the simultaneous consumption of five or more drugs at once, is a prevalent phenomenon in the older population. Some of these polypharmacies, dee…