World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
56
Citations
68733
World Ranking
3926
National Ranking
1861

Overview

Pascal Vincent is affiliated with Facebook in the United States and works primarily in the field of computer science. Their research output spans multiple subfields, including artificial intelligence, computer vision and pattern recognition, radiology, nuclear medicine and imaging, management science and operations research, and computer networks and communications.

The scientist has contributed significantly to various research topics. Among these are domain adaptation and few-shot learning, reinforcement learning in robotics, adversarial robustness in machine learning, multimodal machine learning applications, advanced neural network applications, advanced bandit algorithms research, and optimization and search problems.

Frequent coauthors of Pascal Vincent include Simon Lacoste-Julien, Abdelaziz Touati, Hugo Berard, Nicolas Ballas, and Avishek Joey Bose, reflecting collaboration within a network of researchers.

Pascal Vincent has published extensively in a range of venues. The most frequent publication venues are:

  • arXiv (Cornell University)
  • Radiology Artificial Intelligence
  • Transactions of the Association for Computational Linguistics

Some recent papers authored or coauthored by Pascal Vincent are as follows:

  • "fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning," 2020, Radiology Artificial Intelligence
  • "Understanding Dimensional Collapse in Contrastive Self-supervised Learning," 2021, arXiv (Cornell University)
  • "Stochastic Hamiltonian Gradient Methods for Smooth Games," 2020, arXiv (Cornell University)
  • "Adversarial Example Games," 2020, arXiv (Cornell University)
  • "Stable Policy Optimization via Off-Policy Divergence Regularization," 2020, arXiv (Cornell University)

Best Publications

  • Representation Learning: A Review and New Perspectives

    Y. Bengio;A. Courville;P. Vincent

  • Extracting and composing robust features with denoising autoencoders

    Pascal Vincent;Hugo Larochelle;Yoshua Bengio;Pierre-Antoine Manzagol

  • A neural probabilistic language model

    Yoshua Bengio;Réjean Ducharme;Pascal Vincent;Christian Janvin

  • Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

    Pascal Vincent;Hugo Larochelle;Isabelle Lajoie;Yoshua Bengio

  • Why Does Unsupervised Pre-training Help Deep Learning?

    Dumitru Erhan;Aaron C. Courville;Yoshua Bengio;Pascal Vincent

  • Theano: A Python framework for fast computation of mathematical expressions

    Rami Al-Rfou;Guillaume Alain;Amjad Almahairi

  • Why Does Unsupervised Pre-training Help Deep Learning?

    Dumitru Erhan;Yoshua Bengio;Aaron Courville;Pierre-Antoine Manzagol

  • Contractive Auto-Encoders: Explicit Invariance During Feature Extraction

    Salah Rifai;Pascal Vincent;Xavier Muller;Xavier Glorot

  • Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering

    Yoshua Bengio;Jean-françcois Paiement;Pascal Vincent;Olivier Delalleau

  • A connection between score matching and denoising autoencoders

    Pascal Vincent

  • A Neural Probabilistic Language Model

    Yoshua Bengio;Réjean Ducharme;Pascal Vincent

  • fastMRI: An Open Dataset and Benchmarks for Accelerated MRI.

    Jure Zbontar;Florian Knoll;Anuroop Sriram;Matthew J. Muckley

  • Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives

    Yoshua Bengio;Aaron C. Courville;Pascal Vincent

  • Generalized Denoising Auto-Encoders as Generative Models

    Yoshua Bengio;Li Yao;Guillaume Alain;Pascal Vincent

  • The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training

    Dumitru Erhan;Pierre-Antoine Manzagol;Yoshua Bengio;Samy Bengio

  • Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription

    Nicolas Boulanger-lewandowski;Yoshua Bengio;Pascal Vincent

  • EmoNets: Multimodal deep learning approaches for emotion recognition in video

    Samira Ebrahimi Kahou;Xavier Bouthillier;Pascal Lamblin;Çaglar Gülçehre

  • Combining modality specific deep neural networks for emotion recognition in video

    Samira Ebrahimi Kahou;Christopher Pal;Xavier Bouthillier;Pierre Froumenty

  • Kernel Matching Pursuit

    Pascal Vincent;Yoshua Bengio

  • Unsupervised and Transfer Learning Challenge: a Deep Learning Approach

    Grégoire Mesnil;Yann N. Dauphin;Xavier Glorot;Salah Rifai

Frequent Co-Authors

Yoshua Bengio
Yoshua Bengio University of Montreal
Aaron Courville
Aaron Courville University of Montreal
Yann N. Dauphin
Yann N. Dauphin Google (United States)
Simon Lacoste-Julien
Simon Lacoste-Julien University of Montreal
Chris Pal
Chris Pal Polytechnique Montréal
Nicolas Ballas
Nicolas Ballas Facebook (United States)
Roland Memisevic
Roland Memisevic University of Montreal
Hugo Larochelle
Hugo Larochelle Google (United States)
Léon Bottou
Léon Bottou Facebook (United States)
Dumitru Erhan
Dumitru Erhan Google (United States)

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