1.5k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Université de MontréalCA125 papers
- McGill UniversityCA59 papers
- Polytechnique MontréalCA27 papers
- Flatiron Health (United States)US24 papers
- Flatiron Institute23 papers
- Centre National de la Recherche ScientifiqueFR22 papers
- University of TorontoCA16 papers
- Canadian Institute for Advanced ResearchCA14 papers
- Université Paris-SaclayFR14 papers
- University of AmsterdamNL13 papers
- University of WaterlooCA13 papers
- Centre for Research in Astrophysics of QuébecCA12 papers
13 papers · 2 filters
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…
Proving Linear Mode Connectivity of Neural Networks via Optimal Transport
Damien Ferbach, Baptiste Goujaud, Gauthier Gidel +1
The energy landscape of high-dimensional non-convex optimization problems is crucial to understanding the effectiveness of modern deep neural network architectures. Recent works ha…
Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles
Xing Shen, Hengguan Huang, Brennan Nichyporuk +1
Once deployed, medical image analysis methods are often faced with unexpected image corruptions and noise perturbations. These unknown covariate shifts present significant challeng…
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…
Amplifying Pathological Detection in EEG Signaling Pathways through Cross-Dataset Transfer Learning
Mohammad-Javad Darvishi-Bayazi, Mohammad Sajjad Ghaemi, Timothee Lesort +3
Pathology diagnosis based on EEG signals and decoding brain activity holds immense importance in understanding neurological disorders. With the advancement of artificial intelligen…
How accurate are existing land cover maps for agriculture in Sub-Saharan Africa?
Hannah Kerner, Catherine Nakalembe, Adam Yang +4
Satellite Earth observations (EO) can provide affordable and timely information for assessing crop conditions and food production. Such monitoring systems are essential in Africa,…
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…
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
Federico Berto, Chuanbo Hua, Junyoung Park +30
Combinatorial optimization (CO) is fundamental to several real-world applications, from logistics and scheduling to hardware design and resource allocation. Deep reinforcement lear…
: Accelerating Asynchronous Communication in Decentralized Deep Learning
Adel Nabli, Eugene Belilovsky, Edouard Oyallon
Distributed training of Deep Learning models has been critical to many recent successes in the field. Current standard methods primarily rely on synchronous centralized algorithms…
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…
A Text-guided Protein Design Framework
Shengchao Liu, Yanjing Li, Zhuoxinran Li +10
Current AI-assisted protein design mainly utilizes protein sequential and structural information. Meanwhile, there exists tremendous knowledge curated by humans in the text format…
GFlowNets for AI-Driven Scientific Discovery
Moksh Jain, Tristan Deleu, Jason Hartford +3
Tackling the most pressing problems for humanity, such as the climate crisis and the threat of global pandemics, requires accelerating the pace of scientific discovery. While scien…