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
7 papers · 2 filters
Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs
Stephen Bonner, Ufuk Kirik, Ola Engkvist +2
Adoption of recently developed methods from machine learning has given rise to creation of drug-discovery knowledge graphs (KG) that utilize the interconnected nature of the domain…
Ego-GNNs: Exploiting Ego Structures in Graph Neural Networks
Dylan Sandfelder, Priyesh Vijayan, William L. Hamilton
Graph neural networks (GNNs) have achieved remarkable success as a framework for deep learning on graph-structured data. However, GNNs are fundamentally limited by their tree-struc…
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming
Minkai Xu, Wujie Wang, Shitong Luo +4
Predicting molecular conformations (or 3D structures) from molecular graphs is a fundamental problem in many applications. Most existing approaches are usually divided into two ste…
DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection
Hadi Hojjati, Narges Armanfard
Semi-supervised anomaly detection aims to detect anomalies from normal samples using a model that is trained on normal data. With recent advancements in deep learning, researchers…
Quantization and Deployment of Deep Neural Networks on Microcontrollers
Pierre-Emmanuel Novac, Ghouthi Boukli Hacene, Alain Pegatoquet +2
Embedding Artificial Intelligence onto low-power devices is a challenging task that has been partly overcome with recent advances in machine learning and hardware design. Presently…
A Practical Survey on Faster and Lighter Transformers
Quentin Fournier, Gaétan Marceau Caron, Daniel Aloise
Recurrent neural networks are effective models to process sequences. However, they are unable to learn long-term dependencies because of their inherent sequential nature. As a solu…
Continuum: Simple Management of Complex Continual Learning Scenarios
Arthur Douillard, Timothée Lesort
Continual learning is a machine learning sub-field specialized in settings with non-iid data. Hence, the training data distribution is not static and drifts through time. Those dri…