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
34 papers · 1 filter
MeDAL: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining
Zhi Wen, Xing Han Lu, Siva Reddy
One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets. In this work, we present MeDAL, a large…
Optimizing Deeper Transformers on Small Datasets
Peng Xu, Dhruv Kumar, Wei Yang +6
It is a common belief that training deep transformers from scratch requires large datasets. Consequently, for small datasets, people usually use shallow and simple additional layer…
Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards
Susan Amin, Maziar Gomrokchi, Hossein Aboutalebi +2
A major challenge in reinforcement learning is the design of exploration strategies, especially for environments with sparse reward structures and continuous state and action space…
deep21: a Deep Learning Method for 21cm Foreground Removal
T. Lucas Makinen, Lachlan Lancaster, Francisco Villaescusa-Navarro +4
We seek to remove foreground contaminants from 21cm intensity mapping observations. We demonstrate that a deep convolutional neural network (CNN) with a UNet architecture and three…
Geometric Scattering Attention Networks
Yimeng Min, Frederik Wenkel, Guy Wolf
Geometric scattering has recently gained recognition in graph representation learning, and recent work has shown that integrating scattering features in graph convolution networks…
Attention is All You Need in Speech Separation
Cem Subakan, Mirco Ravanelli, Samuele Cornell +2
Recurrent Neural Networks (RNNs) have long been the dominant architecture in sequence-to-sequence learning. RNNs, however, are inherently sequential models that do not allow parall…
Reflexive Design for Fairness and Other Human Values in Formal Models
Benjamin Fish, Luke Stark
Algorithms and other formal models purportedly incorporating human values like fairness have grown increasingly popular in computer science. In response to sociotechnical challenge…
Maximum Reward Formulation In Reinforcement Learning
Sai Krishna Gottipati, Yashaswi Pathak, Rohan Nuttall +6
Reinforcement learning (RL) algorithms typically deal with maximizing the expected cumulative return (discounted or undiscounted, finite or infinite horizon). However, several cruc…
Machine Learning in Airline Crew Pairing to Construct Initial Clusters for Dynamic Constraint Aggregation
Yassine Yaakoubi, François Soumis, Simon Lacoste-Julien
The crew pairing problem (CPP) is generally modelled as a set partitioning problem where the flights have to be partitioned in pairings. A pairing is a sequence of flight legs sepa…
A Novel Machine Learning Approach to Disentangle Multi-Temperature Regions in Galaxy Clusters
Carter L. Rhea, Julie Hlavacek-Larrondo, Laurence Perreault-Levasseur +2
The hot intra-cluster medium (ICM) surrounding the heart of galaxy clusters is a complex medium comprised of various emitting components. Although previous studies of nearby galaxy…
Deep Generative Models for Galaxy Image Simulations
Francois Lanusse, Rachel Mandelbaum, Siamak Ravanbakhsh +3
Image simulations are essential tools for preparing and validating the analysis of current and future wide-field optical surveys. However, the galaxy models used as the basis for t…
HInet: Generating neutral hydrogen from dark matter with neural networks
Digvijay Wadekar, Francisco Villaescusa-Navarro, Shirley Ho +1
Upcoming 21cm surveys will map the spatial distribution of cosmic neutral hydrogen (HI) over very large cosmological volumes. In order to maximize the scientific return of these su…
Extendable and invertible manifold learning with geometry regularized autoencoders
Andrés F. Duque, Sacha Morin, Guy Wolf +1
A fundamental task in data exploration is to extract simplified low dimensional representations that capture intrinsic geometry in data, especially for faithfully visualizing data…
Data-Efficient Reinforcement Learning with Self-Predictive Representations
Max Schwarzer, Ankesh Anand, Rishab Goel +3
While deep reinforcement learning excels at solving tasks where large amounts of data can be collected through virtually unlimited interaction with the environment, learning from l…
Variational Autoencoders for Anomalous Jet Tagging
Taoli Cheng, Jean-François Arguin, Julien Leissner-Martin +2
We present a detailed study on Variational Autoencoders (VAEs) for anomalous jet tagging at the Large Hadron Collider. By taking in low-level jet constituents' information, and tra…
NeuMiss networks: differentiable programming for supervised learning with missing values
Marine Le Morvan, Julie Josse, Thomas Moreau +2
The presence of missing values makes supervised learning much more challenging. Indeed, previous work has shown that even when the response is a linear function of the complete dat…
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules
Sarthak Mittal, Alex Lamb, Anirudh Goyal +5
Robust perception relies on both bottom-up and top-down signals. Bottom-up signals consist of what's directly observed through sensation. Top-down signals consist of beliefs and ex…
A Comparative Study of Gamma Markov Chains for Temporal Non-Negative Matrix Factorization
Louis Filstroff, Olivier Gouvert, Cédric Févotte +1
Non-negative matrix factorization (NMF) has become a well-established class of methods for the analysis of non-negative data. In particular, a lot of effort has been devoted to pro…
COVID-19 Image Data Collection: Prospective Predictions Are the Future
Joseph Paul Cohen, Paul Morrison, Lan Dao +3
Across the world's coronavirus disease 2019 (COVID-19) hot spots, the need to streamline patient diagnosis and management has become more pressing than ever. As one of the main ima…
PatchUp: A Feature-Space Block-Level Regularization Technique for Convolutional Neural Networks
Mojtaba Faramarzi, Mohammad Amini, Akilesh Badrinaaraayanan +2
Large capacity deep learning models are often prone to a high generalization gap when trained with a limited amount of labeled training data. A recent class of methods to address t…
Entropic Out-of-Distribution Detection: Seamless Detection of Unknown Examples
David Macêdo, Tsang Ing Ren, Cleber Zanchettin +2
In this paper, we argue that the unsatisfactory out-of-distribution (OOD) detection performance of neural networks is mainly due to the SoftMax loss anisotropy and propensity to pr…
Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach
Wenyu Du, Zhouhan Lin, Yikang Shen +3
It is commonly believed that knowledge of syntactic structure should improve language modeling. However, effectively and computationally efficiently incorporating syntactic structu…
A Fast Fully Octave Convolutional Neural Network for Document Image Segmentation
Ricardo Batista das Neves Junior, Luiz Felipe Verçosa, David Macêdo +2
The Know Your Customer (KYC) and Anti Money Laundering (AML) are worldwide practices to online customer identification based on personal identification documents, similarity and li…
Distantly-Supervised Neural Relation Extraction with Side Information using BERT
Johny Moreira, Chaina Oliveira, David Macêdo +2
Relation extraction (RE) consists in categorizing the relationship between entities in a sentence. A recent paradigm to develop relation extractors is Distant Supervision (DS), whi…