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
35 papers · 1 filter
Survey on AI Ethics: A Socio-technical Perspective
Dave Mbiazi, Meghana Bhange, Maryam Babaei +3
The past decade has observed a significant advancement in AI with deep learning-based models being deployed in diverse scenarios, including safety-critical applications. As these A…
The Past, Present, and Future of Typological Databases in NLP
Emi Baylor, Esther Ploeger, Johannes Bjerva
Typological information has the potential to be beneficial in the development of NLP models, particularly for low-resource languages. Unfortunately, current large-scale typological…
A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy
Léo Milecki, Jonathan Porée, Hatim Belgharbi +6
Ultrasound Localization Microscopy can resolve the microvascular bed down to a few micrometers. To achieve such performance microbubble contrast agents must perfuse the entire micr…
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…
Managing extreme AI risks amid rapid progress
Yoshua Bengio, Geoffrey Hinton, Andrew Yao +22
Artificial Intelligence (AI) is progressing rapidly, and companies are shifting their focus to developing generalist AI systems that can autonomously act and pursue goals. Increase…
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…
Towards contrast-agnostic soft segmentation of the spinal cord
Sandrine Bédard, Enamundram Naga Karthik, Charidimos Tsagkas +5
Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and monitoring of cord compression or neurod…
Galaxy Clustering Analysis with SimBIG and the Wavelet Scattering Transform
Bruno Régaldo-Saint Blancard, ChangHoon Hahn, Shirley Ho +8
The non-Gaussisan spatial distribution of galaxies traces the large-scale structure of the Universe and therefore constitutes a prime observable to constrain cosmological parameter…
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…
AstroPhot: Fitting Everything Everywhere All at Once in Astronomical Images
Connor Stone, Stephane Courteau, Jean-Charles Cuillandre +3
We present AstroPhot, a fast, powerful, and user-friendly Python based astronomical image photometry solver. AstroPhot incorporates automatic differentiation and GPU (or parallel C…
Towards General and Efficient Online Tuning for Spark
Yang Li, Huaijun Jiang, Yu Shen +8
The distributed data analytic system -- Spark is a common choice for processing massive volumes of heterogeneous data, while it is challenging to tune its parameters to achieve hig…
Simulation-guided galaxy evolution inference: A case study with strong lensing galaxies
Andreas Filipp, Yiping Shu, Ruediger Pakmor +2
Understanding the evolution of galaxies provides crucial insights into a broad range of aspects in astrophysics, including structure formation and growth, the nature of dark energy…
The CluMPR Galaxy Cluster-Finding Algorithm and DESI Legacy Survey Galaxy Cluster Catalogue
M. J. Yantovski-Barth, Jeffrey A. Newman, Biprateep Dey +4
Galaxy clusters enable unique opportunities to study cosmology, dark matter, galaxy evolution, and strongly-lensed transients. We here present a new cluster-finding algorithm, CluM…
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…
Spatial variations in aromatic hydrocarbon emission in a dust-rich galaxy
Justin S. Spilker, Kedar A. Phadke, Manuel Aravena +35
Dust grains absorb half of the radiation emitted by stars throughout the history of the universe, re-emitting this energy at infrared wavelengths. Polycyclic aromatic hydrocarbons…
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…
Evaluating the Social Impact of Generative AI Systems in Systems and Society
Irene Solaiman, Zeerak Talat, William Agnew +28
Generative AI systems across modalities, ranging from text (including code), image, audio, and video, have broad social impacts, but there is no official standard for means of eval…
Optimal Approximate Minimization of One-Letter Weighted Finite Automata
Clara Lacroce, Borja Balle, Prakash Panangaden +1
In this paper, we study the approximate minimization problem of weighted finite automata (WFAs): to compute the best possible approximation of a WFA given a bound on the number of…
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…