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
19 papers · 1 filter
Speech recognition with quaternion neural networks
Titouan Parcollet, Mirco Ravanelli, Mohamed Morchid +2
Neural network architectures are at the core of powerful automatic speech recognition systems (ASR). However, while recent researches focus on novel model architectures, the acoust…
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
Weiping Song, Chence Shi, Zhiping Xiao +4
Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking on an ad or an item, is critical to many online applications such as online advertisin…
Interpretable Neuron Structuring with Graph Spectral Regularization
Alexander Tong, David van Dijk, Jay S. Stanley +6
While neural networks are powerful approximators used to classify or embed data into lower dimensional spaces, they are often regarded as black boxes with uninterpretable features.…
Compressing the Input for CNNs with the First-Order Scattering Transform
Edouard Oyallon, Eugene Belilovsky, Sergey Zagoruyko +1
We study the first-order scattering transform as a candidate for reducing the signal processed by a convolutional neural network (CNN). We show theoretical and empirical evidence t…
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Zhilin Yang, Peng Qi, Saizheng Zhang +4
Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. We introduce HotpotQA, a new dataset with 113k…
Scattering Networks for Hybrid Representation Learning
Edouard Oyallon, Sergey Zagoruyko, Gabriel Huang +4
Scattering networks are a class of designed Convolutional Neural Networks (CNNs) with fixed weights. We argue they can serve as generic representations for modelling images. In par…
Deep Graph Infomax
Petar Veličković, William Fedus, William L. Hamilton +3
We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner. DGI relies on maximizing mutual in…
DeepInf: Social Influence Prediction with Deep Learning
Jiezhong Qiu, Jian Tang, Hao Ma +3
Social and information networking activities such as on Facebook, Twitter, WeChat, and Weibo have become an indispensable part of our everyday life, where we can easily access frie…
Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information
Eric Larsen, Sébastien Lachapelle, Yoshua Bengio +3
This paper offers a methodological contribution at the intersection of machine learning and operations research. Namely, we propose a methodology to quickly predict expected tactic…
Safe Option-Critic: Learning Safety in the Option-Critic Architecture
Arushi Jain, Khimya Khetarpal, Doina Precup
Designing hierarchical reinforcement learning algorithms that exhibit safe behaviour is not only vital for practical applications but also, facilitates a better understanding of an…
Rigorous asymptotics of a KdV soliton gas
Manuela Girotti, Tamara Grava, Robert Jenkins +1
We analytically study the long time and large space asymptotics of a new broad class of solutions of the KdV equation introduced by Dyachenko, Zakharov, and Zakharov. These solutio…
Straight to the Tree: Constituency Parsing with Neural Syntactic Distance
Yikang Shen, Zhouhan Lin, Athul Paul Jacob +3
In this work, we propose a novel constituency parsing scheme. The model predicts a vector of real-valued scalars, named syntactic distances, for each split position in the input se…
BFGAN: Backward and Forward Generative Adversarial Networks for Lexically Constrained Sentence Generation
Dayiheng Liu, Jie Fu, Qian Qu +1
Incorporating prior knowledge like lexical constraints into the model's output to generate meaningful and coherent sentences has many applications in dialogue system, machine trans…
Quaternion Recurrent Neural Networks
Titouan Parcollet, Mirco Ravanelli, Mohamed Morchid +4
Recurrent neural networks (RNNs) are powerful architectures to model sequential data, due to their capability to learn short and long-term dependencies between the basic elements o…
Image-to-image translation for cross-domain disentanglement
Abel Gonzalez-Garcia, Joost van de Weijer, Yoshua Bengio
Deep image translation methods have recently shown excellent results, outputting high-quality images covering multiple modes of the data distribution. There has also been increased…
Neural Autoregressive Flows
Chin-Wei Huang, David Krueger, Alexandre Lacoste +1
Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, via Masked Autoregressive Flows (MAF), and to…
Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Endpoint Prediction
Luchen Liu, Jianhao Shen, Ming Zhang +2
The availability of a large amount of electronic health records (EHR) provides huge opportunities to improve health care service by mining these data. One important application is…
Learning Anonymized Representations with Adversarial Neural Networks
Clément Feutry, Pablo Piantanida, Yoshua Bengio +1
Statistical methods protecting sensitive information or the identity of the data owner have become critical to ensure privacy of individuals as well as of organizations. This paper…
Personalizing Dialogue Agents: I have a dog, do you have pets too?
Saizheng Zhang, Emily Dinan, Jack Urbanek +3
Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating. In this work we present the…