Pascal Vincent

research

∙ 08/08/2023

PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning

Synthetic image datasets offer unmatched advantages for designing and ev...

0 Florian Bordes, et al. ∙

research

∙ 07/31/2023

Predicting masked tokens in stochastic locations improves masked image modeling

Self-supervised learning is a promising paradigm in deep learning that e...

0 Amir Bar, et al. ∙

research

∙ 06/28/2023

Identifiability of Discretized Latent Coordinate Systems via Density Landmarks Detection

Disentanglement aims to recover meaningful latent ground-truth factors f...

0 Vitória Barin Pacela, et al. ∙

research

∙ 04/26/2023

Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning

Self-supervised learning (SSL) algorithms can produce useful image repre...

0 Casey Meehan, et al. ∙

research

∙ 04/25/2023

Objectives Matter: Understanding the Impact of Self-Supervised Objectives on Vision Transformer Representations

Joint-embedding based learning (e.g., SimCLR, MoCo, DINO) and reconstruc...

0 Shashank Shekhar, et al. ∙

research

∙ 04/11/2023

A surprisingly simple technique to control the pretraining bias for better transfer: Expand or Narrow your representation

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

0 Florian Bordes, et al. ∙

research

∙ 03/16/2023

Instance-Conditioned GAN Data Augmentation for Representation Learning

Data augmentation has become a crucial component to train state-of-the-a...

0 Pietro Astolfi, et al. ∙

research

∙ 03/03/2023

Towards Democratizing Joint-Embedding Self-Supervised Learning

Joint Embedding Self-Supervised Learning (JE-SSL) has seen rapid develop...

0 Florian Bordes, et al. ∙

research

∙ 01/19/2023

Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture

This paper demonstrates an approach for learning highly semantic image r...

2 Mahmoud Assran, et al. ∙

research

∙ 11/03/2022

ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations

Deep learning vision systems are widely deployed across applications whe...

2 Badr Youbi Idrissi, et al. ∙

research

∙ 10/13/2022

Disentanglement of Correlated Factors via Hausdorff Factorized Support

A grand goal in deep learning research is to learn representations capab...

35 Karsten Roth, et al. ∙

research

∙ 10/13/2022

The Hidden Uniform Cluster Prior in Self-Supervised Learning

A successful paradigm in representation learning is to perform self-supe...

0 Mahmoud Assran, et al. ∙

research

∙ 06/27/2022

Guillotine Regularization: Improving Deep Networks Generalization by Removing their Head

One unexpected technique that emerged in recent years consists in traini...

33 Florian Bordes, et al. ∙

research

∙ 04/14/2022

Masked Siamese Networks for Label-Efficient Learning

We propose Masked Siamese Networks (MSN), a self-supervised learning fra...

25 Mahmoud Assran, et al. ∙

research

∙ 12/16/2021

High Fidelity Visualization of What Your Self-Supervised Representation Knows About

Discovering what is learned by neural networks remains a challenge. In s...

7 Florian Bordes, et al. ∙

research

∙ 03/02/2021

Online Adversarial Attacks

Adversarial attacks expose important vulnerabilities of deep learning mo...

0 Andjela Mladenovic, et al. ∙

research

∙ 03/01/2021

Accounting for Variance in Machine Learning Benchmarks

Strong empirical evidence that one machine-learning algorithm A outperfo...

23 Xavier Bouthillier, et al. ∙

research

∙ 10/24/2020

Efficient Learning in Non-Stationary Linear Markov Decision Processes

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

2 Ahmed Touati, et al. ∙

research

∙ 08/03/2020

Implicit Regularization in Deep Learning: A View from Function Space

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

39 Aristide Baratin, et al. ∙

research

∙ 07/08/2020

Stochastic Hamiltonian Gradient Methods for Smooth Games

The success of adversarial formulations in machine learning has brought ...

45 Nicolas Loizou, et al. ∙

research

∙ 07/07/2020

Sharp Analysis of Smoothed Bellman Error Embedding

The Smoothed Bellman Error Embedding algorithm <cit.>, known as SBEED, w...

0 Ahmed Touati, et al. ∙

research

∙ 07/01/2020

Adversarial Example Games

The existence of adversarial examples capable of fooling trained neural ...

13 Avishek Joey Bose, et al. ∙

research

∙ 06/22/2020

Revisiting Loss Modelling for Unstructured Pruning

By removing parameters from deep neural networks, unstructured pruning m...

0 César Laurent, et al. ∙

research

∙ 04/16/2020

Do sequence-to-sequence VAEs learn global features of sentences?

A longstanding goal in NLP is to compute global sentence representations...

0 Tom Bosc, et al. ∙

research

∙ 03/09/2020

Stable Policy Optimization via Off-Policy Divergence Regularization

Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization...

6 Ahmed Touati, et al. ∙

research

∙ 07/31/2019

An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation

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

1 Vincent Michalski, et al. ∙

research

∙ 06/11/2019

A Closer Look at the Optimization Landscapes of Generative Adversarial Networks

Generative adversarial networks have been very successful in generative ...

1 Hugo Berard, et al. ∙

research

∙ 06/10/2019

Stochastic Neural Network with Kronecker Flow

Recent advances in variational inference enable the modelling of highly ...

1 Chin-Wei Huang, et al. ∙

research

∙ 06/09/2019

SVRG for Policy Evaluation with Fewer Gradient Evaluations

Stochastic variance-reduced gradient (SVRG) is an optimization method or...

0 Zilun Peng, et al. ∙

research

∙ 02/08/2019

Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisition

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

0 Zizhao Zhang, et al. ∙

research

∙ 11/21/2018

fastMRI: An Open Dataset and Benchmarks for Accelerated MRI

Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measuremen...

21 Jure Žbontar, et al. ∙

research

∙ 06/11/2018

Fast Approximate Natural Gradient Descent in a Kronecker-factored Eigenbasis

Optimization algorithms that leverage gradient covariance information, s...

0 Thomas George, et al. ∙

research

∙ 06/06/2018

Randomized Value Functions via Multiplicative Normalizing Flows

Randomized value functions offer a promising approach towards the challe...

0 Ahmed Touati, et al. ∙

research

∙ 02/28/2018

A Variational Inequality Perspective on Generative Adversarial Nets

Stability has been a recurrent issue in training generative adversarial ...

0 Gauthier Gidel, et al. ∙

research

∙ 09/05/2017

Improving Landmark Localization with Semi-Supervised Learning

We present two techniques to improve landmark localization from partiall...

0 Sina Honari, et al. ∙

research

∙ 08/08/2017

Parametric Adversarial Divergences are Good Task Losses for Generative Modeling

Generative modeling of high dimensional data like images is a notoriousl...

1 Gabriel Huang, et al. ∙

research

∙ 06/01/2017

Learning to Compute Word Embeddings On the Fly

Words in natural language follow a Zipfian distribution whereby some wor...

0 Dzmitry Bahdanau, et al. ∙

research

∙ 05/25/2017

Convergent Tree-Backup and Retrace with Function Approximation

Off-policy learning is key to scaling up reinforcement learning as it al...

0 Ahmed Touati, et al. ∙

research

∙ 03/20/2017

Learning to Generate Samples from Noise through Infusion Training

In this work, we investigate a novel training procedure to learn a gener...

0 Florian Bordes, et al. ∙

research

∙ 09/19/2016

A Cheap Linear Attention Mechanism with Fast Lookups and Fixed-Size Representations

The softmax content-based attention mechanism has proven to be very bene...

0 Alexandre de Brébisson, et al. ∙

research

∙ 06/26/2016

Exact gradient updates in time independent of output size for the spherical loss family

An important class of problems involves training deep neural networks wi...

0 Pascal Vincent, et al. ∙

research

∙ 05/24/2016

Hierarchical Memory Networks

Memory networks are neural networks with an explicit memory component th...

0 Sarath Chandar, et al. ∙

research

∙ 05/09/2016

Theano: A Python framework for fast computation of mathematical expressions

Theano is a Python library that allows to define, optimize, and evaluate...

0 The Theano Development Team, et al. ∙

research

∙ 04/29/2016

The Z-loss: a shift and scale invariant classification loss belonging to the Spherical Family

Despite being the standard loss function to train multi-class neural net...

0 Alexandre de Brébisson, et al. ∙

research

∙ 11/23/2015

Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation

Deep neural networks with alternating convolutional, max-pooling and dec...

0 Sina Honari, et al. ∙

research

∙ 11/16/2015

An Exploration of Softmax Alternatives Belonging to the Spherical Loss Family

In a multi-class classification problem, it is standard to model the out...

0 Alexandre de Brébisson, et al. ∙

research

∙ 07/31/2015

Artificial Neural Networks Applied to Taxi Destination Prediction

We describe our first-place solution to the ECML/PKDD discovery challeng...

0 Alexandre de Brébisson, et al. ∙

research

∙ 07/21/2015

Clustering is Efficient for Approximate Maximum Inner Product Search

Efficient Maximum Inner Product Search (MIPS) is an important task that ...

0 Alex Auvolat, et al. ∙

research

∙ 06/29/2015

Dropout as data augmentation

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

0 Xavier Bouthillier, et al. ∙

research

∙ 03/18/2015

GSNs : Generative Stochastic Networks

We introduce a novel training principle for probabilistic models that is...

0 Guillaume Alain, et al. ∙

Pascal Vincent

Featured Co-authors