5 papers
CanViT: Toward Active-Vision Foundation Models
Yohaï-Eliel Berreby, Sabrina Du, Audrey Durand +1
Active computer vision promises efficient, biologically plausible perception through sequential, localized glimpses, but lacks scalable general-purpose architectures and pretrainin…
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 proportionally to their a priori unknown value, their reward.…
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…
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…
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…