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Pascal Vincent

41 papers · 2003–2025 · 11 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🐝 Cross-Pollinator (13) πŸƒ Academic Marathon (22) 🌈 Renaissance Researcher (8) 🌍 Conference Polyglot (11) πŸŒ‰ Interdisciplinary Bridge 🐣 Hot Topic Early Bird πŸ—ΊοΈ Taxonomy Completionist (14) 🌟 Keyword Trendsetter Combo (4) πŸ‘‘ Triple Crown 🧬 Topic Evolution πŸ† Keyword Champion 🌱 Topic Pioneer πŸ’Ž Century Club (41) ⚑ Prolific Year (6) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter ❓ The Questioner (4) πŸ”₯ Unstoppable (8) πŸ—ƒοΈ Keyword Collector (144)

Conferences

ICLR (10) NIPS (7) ICML (6) AISTATS (4) CVPR (4) JMLR (3) EMNLP (2) UAI (2) CLEAR (1) ECCV (1) IJCAI (1)

Top co-authors

Nicolas Ballas (7) Simon Lacoste-Julien (7) Yoshua Bengio (7) Florian Bordes (5) Ahmed Touati (5) Hugo Berard (5) Aaron Courville (4) Diane Bouchacourt (4) Gauthier Gidel (4) Mark Ibrahim (4)

Keywords

representation learning (7) neural network (3) semi-supervised learning (2) denoising autoencoder (2) unsupervised pre-training (2) deep belief network (2) gradient descent (2) self-supervised learning (2) thompson sampling (2) unsupervised learning (2) contractive autoencoders (2) feature learning (2) language model (2) word embedding (2) deep reinforcement learning (2) variational inference (2) deep learning (2) markov chain monte carlo (2) image generation (1) policy evaluation (1)

Papers

The Pitfalls of Memorization: When Memorization Hurts Generalization ICLR 2025
MaestroMotif: Skill Design from Artificial Intelligence Feedback ICLR 2025
Compositional Risk Minimization ICML 2025
Discovering Environments with XRM ICML 2024
On the Identifiability of Quantized Factors CLEAR 2024
Motif: Intrinsic Motivation from Artificial Intelligence Feedback ICLR 2024
Stochastic positional embeddings improve masked image modeling ICML 2024
Self-Supervised Learning From Images With a Joint-Embedding Predictive Architecture CVPR 2023
Do SSL Models Have DΓ©jΓ  Vu? A Case of Unintended Memorization in Self-supervised Learning NIPS 2023
PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning NIPS 2023
ImageNet-X: Understanding Model Mistakes with Factor of Variation Annotations ICLR 2023
The hidden uniform cluster prior in self-supervised learning ICLR 2023
Disentanglement of Correlated Factors via Hausdorff Factorized Support ICLR 2023
Understanding Dimensional Collapse in Contrastive Self-supervised Learning ICLR 2022
Online Adversarial Attacks ICLR 2022
Masked Siamese Networks for Label-Efficient Learning ECCV 2022
Implicit Regularization via Neural Feature Alignment AISTATS 2021
Stochastic Hamiltonian Gradient Methods for Smooth Games ICML 2020
Stable Policy Optimization via Off-Policy Divergence Regularization UAI 2020
Stochastic Neural Network with Kronecker Flow AISTATS 2020
SVRG for Policy Evaluation with Fewer Gradient Evaluations IJCAI 2020
Adversarial Example Games NIPS 2020
A Closer Look at the Optimization Landscapes of Generative Adversarial Networks ICLR 2020
Do sequence-to-sequence VAEs learn global features of sentences? EMNLP 2020
A Variational Inequality Perspective on Generative Adversarial Networks ICLR 2019
Unreproducible Research is Reproducible ICML 2019
Randomized Value Functions via Multiplicative Normalizing Flows UAI 2019
Reducing Uncertainty in Undersampled MRI Reconstruction With Active Acquisition CVPR 2019
Convergent Tree Backup and Retrace with Function Approximation ICML 2018
Improving Landmark Localization With Semi-Supervised Learning CVPR 2018
Auto-Encoding Dictionary Definitions into Consistent Word Embeddings EMNLP 2018
Fast Approximate Natural Gradient Descent in a Kronecker Factored Eigenbasis NIPS 2018
Recombinator Networks: Learning Coarse-To-Fine Feature Aggregation CVPR 2016
Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets NIPS 2015
Generalized Denoising Auto-Encoders as Generative Models NIPS 2013
The Manifold Tangent Classifier NIPS 2011
Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines AISTATS 2010
Why Does Unsupervised Pre-training Help Deep Learning? AISTATS 2010
Why Does Unsupervised Pre-training Help Deep Learning? JMLR 2010
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion JMLR 2010
A Neural Probabilistic Language Model JMLR 2003