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1 code implementation • 8 Aug 2023 • Florian Bordes, Shashank Shekhar, Mark Ibrahim, Diane Bouchacourt, Pascal Vincent, Ari S. Morcos

Synthetic image datasets offer unmatched advantages for designing and evaluating deep neural networks: they make it possible to (i) render as many data samples as needed, (ii) precisely control each scene and yield granular ground truth labels (and captions), (iii) precisely control distribution shifts between training and testing to isolate variables of interest for sound experimentation.

no code implementations • 31 Jul 2023 • Amir Bar, Florian Bordes, Assaf Shocher, Mahmoud Assran, Pascal Vincent, Nicolas Ballas, Trevor Darrell, Amir Globerson, Yann Lecun

Specifically, we condition the model on stochastic masked token positions to guide the model toward learning features that are more robust to location uncertainties.

no code implementations • 28 Jun 2023 • Vitória Barin-Pacela, Kartik Ahuja, Simon Lacoste-Julien, Pascal Vincent

Disentanglement aims to recover meaningful latent ground-truth factors from only the observed distribution.

1 code implementation • 26 Apr 2023 • Casey Meehan, Florian Bordes, Pascal Vincent, Kamalika Chaudhuri, Chuan Guo

Self-supervised learning (SSL) algorithms can produce useful image representations by learning to associate different parts of natural images with one another.

no code implementations • 25 Apr 2023 • Shashank Shekhar, Florian Bordes, Pascal Vincent, Ari Morcos

Here, we aim to explain these differences by analyzing the impact of these objectives on the structure and transferability of the learned representations.

no code implementations • 11 Apr 2023 • Florian Bordes, Samuel Lavoie, Randall Balestriero, Nicolas Ballas, Pascal Vincent

Self-Supervised Learning (SSL) models rely on a pretext task to learn representations.

no code implementations • 16 Mar 2023 • Pietro Astolfi, Arantxa Casanova, Jakob Verbeek, Pascal Vincent, Adriana Romero-Soriano, Michal Drozdzal

We showcase the benefits of DA_IC-GAN by plugging it out-of-the-box into the supervised training of ResNets and DeiT models on the ImageNet dataset, and achieving accuracy boosts up to between 1%p and 2%p with the highest capacity models.

1 code implementation • 3 Mar 2023 • Florian Bordes, Randall Balestriero, Pascal Vincent

Joint Embedding Self-Supervised Learning (JE-SSL) has seen rapid developments in recent years, due to its promise to effectively leverage large unlabeled data.

3 code implementations • CVPR 2023 • Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann Lecun, Nicolas Ballas

This paper demonstrates an approach for learning highly semantic image representations without relying on hand-crafted data-augmentations.

no code implementations • 3 Nov 2022 • Badr Youbi Idrissi, Diane Bouchacourt, Randall Balestriero, Ivan Evtimov, Caner Hazirbas, Nicolas Ballas, Pascal Vincent, Michal Drozdzal, David Lopez-Paz, Mark Ibrahim

Equipped with ImageNet-X, we investigate 2, 200 current recognition models and study the types of mistakes as a function of model's (1) architecture, e. g. transformer vs. convolutional, (2) learning paradigm, e. g. supervised vs. self-supervised, and (3) training procedures, e. g., data augmentation.

1 code implementation • 13 Oct 2022 • Karsten Roth, Mark Ibrahim, Zeynep Akata, Pascal Vincent, Diane Bouchacourt

We show that the use of HFS consistently facilitates disentanglement and recovery of ground-truth factors across a variety of correlation settings and benchmarks, even under severe training correlations and correlation shifts, with in parts over $+60\%$ in relative improvement over existing disentanglement methods.

no code implementations • 13 Oct 2022 • Mahmoud Assran, Randall Balestriero, Quentin Duval, Florian Bordes, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Nicolas Ballas

A successful paradigm in representation learning is to perform self-supervised pretraining using tasks based on mini-batch statistics (e. g., SimCLR, VICReg, SwAV, MSN).

no code implementations • 27 Jun 2022 • Florian Bordes, Randall Balestriero, Quentin Garrido, Adrien Bardes, Pascal Vincent

This is a little vexing, as one would hope that the network layer at which invariance is explicitly enforced by the SSL criterion during training (the last projector layer) should be the one to use for best generalization performance downstream.

2 code implementations • 14 Apr 2022 • Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas

We propose Masked Siamese Networks (MSN), a self-supervised learning framework for learning image representations.

Self-Supervised Image Classification
Self-Supervised Learning
**+1**

2 code implementations • 16 Dec 2021 • Florian Bordes, Randall Balestriero, Pascal Vincent

Discovering what is learned by neural networks remains a challenge.

1 code implementation • ICLR 2022 • Li Jing, Pascal Vincent, Yann Lecun, Yuandong Tian

It has been shown that non-contrastive methods suffer from a lesser collapse problem of a different nature: dimensional collapse, whereby the embedding vectors end up spanning a lower-dimensional subspace instead of the entire available embedding space.

1 code implementation • ICLR 2022 • Andjela Mladenovic, Avishek Joey Bose, Hugo Berard, William L. Hamilton, Simon Lacoste-Julien, Pascal Vincent, Gauthier Gidel

Adversarial attacks expose important vulnerabilities of deep learning models, yet little attention has been paid to settings where data arrives as a stream.

no code implementations • 1 Mar 2021 • Xavier Bouthillier, Pierre Delaunay, Mirko Bronzi, Assya Trofimov, Brennan Nichyporuk, Justin Szeto, Naz Sepah, Edward Raff, Kanika Madan, Vikram Voleti, Samira Ebrahimi Kahou, Vincent Michalski, Dmitriy Serdyuk, Tal Arbel, Chris Pal, Gaël Varoquaux, Pascal Vincent

Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the learning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices.

no code implementations • 1 Jan 2021 • Anthony Ortiz, Kris Sankaran, Olac Fuentes, Christopher Kiekintveld, Pascal Vincent, Yoshua Bengio, Doina Precup

In this work we tackle the problem of out-of-distribution generalization through conditional computation.

no code implementations • 24 Oct 2020 • Ahmed Touati, Pascal Vincent

We study episodic reinforcement learning in non-stationary linear (a. k. a.

1 code implementation • NeurIPS Workshop DL-IG 2020 • Aristide Baratin, Thomas George, César Laurent, R. Devon Hjelm, Guillaume Lajoie, Pascal Vincent, Simon Lacoste-Julien

We approach the problem of implicit regularization in deep learning from a geometrical viewpoint.

no code implementations • ICML 2020 • Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau, Pascal Vincent, Simon Lacoste-Julien, Ioannis Mitliagkas

The success of adversarial formulations in machine learning has brought renewed motivation for smooth games.

no code implementations • 7 Jul 2020 • Ahmed Touati, Pascal Vincent

The \textit{Smoothed Bellman Error Embedding} algorithm~\citep{dai2018sbeed}, known as SBEED, was proposed as a provably convergent reinforcement learning algorithm with general nonlinear function approximation.

1 code implementation • NeurIPS 2020 • Avishek Joey Bose, Gauthier Gidel, Hugo Berard, Andre Cianflone, Pascal Vincent, Simon Lacoste-Julien, William L. Hamilton

We introduce Adversarial Example Games (AEG), a framework that models the crafting of adversarial examples as a min-max game between a generator of attacks and a classifier.

1 code implementation • 22 Jun 2020 • César Laurent, Camille Ballas, Thomas George, Nicolas Ballas, Pascal Vincent

By removing parameters from deep neural networks, unstructured pruning methods aim at cutting down memory footprint and computational cost, while maintaining prediction accuracy.

no code implementations • EMNLP 2020 • Tom Bosc, Pascal Vincent

Using this method, we find that VAEs are prone to memorizing the first words and the sentence length, producing local features of limited usefulness.

1 code implementation • 9 Mar 2020 • Ahmed Touati, Amy Zhang, Joelle Pineau, Pascal Vincent

Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO) are among the most successful policy gradient approaches in deep reinforcement learning (RL).

no code implementations • 31 Jul 2019 • Vincent Michalski, Vikram Voleti, Samira Ebrahimi Kahou, Anthony Ortiz, Pascal Vincent, Chris Pal, Doina Precup

Batch normalization has been widely used to improve optimization in deep neural networks.

1 code implementation • ICLR 2020 • Hugo Berard, Gauthier Gidel, Amjad Almahairi, Pascal Vincent, Simon Lacoste-Julien

Generative adversarial networks have been very successful in generative modeling, however they remain relatively challenging to train compared to standard deep neural networks.

no code implementations • 10 Jun 2019 • Chin-wei Huang, Ahmed Touati, Pascal Vincent, Gintare Karolina Dziugaite, Alexandre Lacoste, Aaron Courville

Recent advances in variational inference enable the modelling of highly structured joint distributions, but are limited in their capacity to scale to the high-dimensional setting of stochastic neural networks.

1 code implementation • 9 Jun 2019 • Zilun Peng, Ahmed Touati, Pascal Vincent, Doina Precup

SVRG was later shown to work for policy evaluation, a problem in reinforcement learning in which one aims to estimate the value function of a given policy.

1 code implementation • CVPR 2019 • Zizhao Zhang, Adriana Romero, Matthew J. Muckley, Pascal Vincent, Lin Yang, Michal Drozdzal

The goal of MRI reconstruction is to restore a high fidelity image from partially observed measurements.

no code implementations • NeurIPS 2018 • Thomas George, César Laurent, Xavier Bouthillier, Nicolas Ballas, Pascal Vincent

Optimization algorithms that leverage gradient covariance information, such as variants of natural gradient descent (Amari, 1998), offer the prospect of yielding more effective descent directions.

11 code implementations • 21 Nov 2018 • Jure Zbontar, Florian Knoll, Anuroop Sriram, Tullie Murrell, Zhengnan Huang, Matthew J. Muckley, Aaron Defazio, Ruben Stern, Patricia Johnson, Mary Bruno, Marc Parente, Krzysztof J. Geras, Joe Katsnelson, Hersh Chandarana, Zizhao Zhang, Michal Drozdzal, Adriana Romero, Michael Rabbat, Pascal Vincent, Nafissa Yakubova, James Pinkerton, Duo Wang, Erich Owens, C. Lawrence Zitnick, Michael P. Recht, Daniel K. Sodickson, Yvonne W. Lui

Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive.

2 code implementations • EMNLP 2018 • Tom Bosc, Pascal Vincent

Monolingual dictionaries are widespread and semantically rich resources.

4 code implementations • 11 Jun 2018 • Thomas George, César Laurent, Xavier Bouthillier, Nicolas Ballas, Pascal Vincent

Optimization algorithms that leverage gradient covariance information, such as variants of natural gradient descent (Amari, 1998), offer the prospect of yielding more effective descent directions.

1 code implementation • 6 Jun 2018 • Ahmed Touati, Harsh Satija, Joshua Romoff, Joelle Pineau, Pascal Vincent

In particular, we augment DQN and DDPG with multiplicative normalizing flows in order to track a rich approximate posterior distribution over the parameters of the value function.

1 code implementation • ICLR 2019 • Gauthier Gidel, Hugo Berard, Gaëtan Vignoud, Pascal Vincent, Simon Lacoste-Julien

Generative adversarial networks (GANs) form a generative modeling approach known for producing appealing samples, but they are notably difficult to train.

no code implementations • CVPR 2018 • Sina Honari, Pavlo Molchanov, Stephen Tyree, Pascal Vincent, Christopher Pal, Jan Kautz

First, we propose the framework of sequential multitasking and explore it here through an architecture for landmark localization where training with class labels acts as an auxiliary signal to guide the landmark localization on unlabeled data.

Ranked #40 on Face Alignment on 300W

no code implementations • ICLR 2018 • Gabriel Huang, Hugo Berard, Ahmed Touati, Gauthier Gidel, Pascal Vincent, Simon Lacoste-Julien

Parametric adversarial divergences, which are a generalization of the losses used to train generative adversarial networks (GANs), have often been described as being approximations of their nonparametric counterparts, such as the Jensen-Shannon divergence, which can be derived under the so-called optimal discriminator assumption.

no code implementations • ICLR 2018 • Dzmitry Bahdanau, Tom Bosc, Stanisław Jastrzębski, Edward Grefenstette, Pascal Vincent, Yoshua Bengio

Words in natural language follow a Zipfian distribution whereby some words are frequent but most are rare.

Ranked #48 on Question Answering on SQuAD1.1 dev

no code implementations • ICML 2018 • Ahmed Touati, Pierre-Luc Bacon, Doina Precup, Pascal Vincent

Off-policy learning is key to scaling up reinforcement learning as it allows to learn about a target policy from the experience generated by a different behavior policy.

1 code implementation • 20 Mar 2017 • Florian Bordes, Sina Honari, Pascal Vincent

In this work, we investigate a novel training procedure to learn a generative model as the transition operator of a Markov chain, such that, when applied repeatedly on an unstructured random noise sample, it will denoise it into a sample that matches the target distribution from the training set.

no code implementations • 19 Sep 2016 • Alexandre de Brébisson, Pascal Vincent

These two limitations restrict the use of the softmax attention mechanism to relatively small-scale applications with short sequences and few lookups per sequence.

no code implementations • 26 Jun 2016 • Pascal Vincent, Alexandre de Brébisson, Xavier Bouthillier

An important class of problems involves training deep neural networks with sparse prediction targets of very high dimension D. These occur naturally in e. g. neural language models or the learning of word-embeddings, often posed as predicting the probability of next words among a vocabulary of size D (e. g. 200, 000).

no code implementations • 24 May 2016 • Sarath Chandar, Sungjin Ahn, Hugo Larochelle, Pascal Vincent, Gerald Tesauro, Yoshua Bengio

In this paper, we explore a form of hierarchical memory network, which can be considered as a hybrid between hard and soft attention memory networks.

1 code implementation • 9 May 2016 • The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre-Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrancois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang

Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.

1 code implementation • 29 Apr 2016 • Alexandre de Brébisson, Pascal Vincent

In this paper, we introduce an alternative classification loss function, the Z-loss, which is designed to address these two issues.

1 code implementation • CVPR 2016 • Sina Honari, Jason Yosinski, Pascal Vincent, Christopher Pal

Deep neural networks with alternating convolutional, max-pooling and decimation layers are widely used in state of the art architectures for computer vision.

1 code implementation • 16 Nov 2015 • Alexandre de Brébisson, Pascal Vincent

In particular, we focus our investigation on spherical bounds of the log-softmax loss and on two spherical log-likelihood losses, namely the log-Spherical Softmax suggested by Vincent et al. (2015) and the log-Taylor Softmax that we introduce.

1 code implementation • 31 Jul 2015 • Alexandre de Brébisson, Étienne Simon, Alex Auvolat, Pascal Vincent, Yoshua Bengio

We describe our first-place solution to the ECML/PKDD discovery challenge on taxi destination prediction.

no code implementations • 21 Jul 2015 • Alex Auvolat, Sarath Chandar, Pascal Vincent, Hugo Larochelle, Yoshua Bengio

Efficient Maximum Inner Product Search (MIPS) is an important task that has a wide applicability in recommendation systems and classification with a large number of classes.

no code implementations • 29 Jun 2015 • Xavier Bouthillier, Kishore Konda, Pascal Vincent, Roland Memisevic

Dropout is typically interpreted as bagging a large number of models sharing parameters.

no code implementations • 18 Mar 2015 • Guillaume Alain, Yoshua Bengio, Li Yao, Jason Yosinski, Eric Thibodeau-Laufer, Saizheng Zhang, Pascal Vincent

We introduce a novel training principle for probabilistic models that is an alternative to maximum likelihood.

no code implementations • 5 Mar 2015 • Samira Ebrahimi Kahou, Xavier Bouthillier, Pascal Lamblin, Caglar Gulcehre, Vincent Michalski, Kishore Konda, Sébastien Jean, Pierre Froumenty, Yann Dauphin, Nicolas Boulanger-Lewandowski, Raul Chandias Ferrari, Mehdi Mirza, David Warde-Farley, Aaron Courville, Pascal Vincent, Roland Memisevic, Christopher Pal, Yoshua Bengio

The task of the emotion recognition in the wild (EmotiW) Challenge is to assign one of seven emotions to short video clips extracted from Hollywood style movies.

1 code implementation • NeurIPS 2015 • Pascal Vincent, Alexandre de Brébisson, Xavier Bouthillier

An important class of problems involves training deep neural networks with sparse prediction targets of very high dimension D. These occur naturally in e. g. neural language models or the learning of word-embeddings, often posed as predicting the probability of next words among a vocabulary of size D (e. g. 200 000).

1 code implementation • NeurIPS 2013 • Yoshua Bengio, Li Yao, Guillaume Alain, Pascal Vincent

Recent work has shown how denoising and contractive autoencoders implicitly capture the structure of the data-generating density, in the case where the corruption noise is Gaussian, the reconstruction error is the squared error, and the data is continuous-valued.

no code implementations • 27 Jun 2012 • Nicolas Boulanger-Lewandowski, Yoshua Bengio, Pascal Vincent

We investigate the problem of modeling symbolic sequences of polyphonic music in a completely general piano-roll representation.

Ranked #5 on Music Modeling on JSB Chorales

5 code implementations • 24 Jun 2012 • Yoshua Bengio, Aaron Courville, Pascal Vincent

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data.

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