Gauthier Gidel

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 18 papers; trusts 620 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

Artificial Intelligence — 377 citations into it; #7,105 of 35,345 by trust here, #7,858 by citations
Optimization & Training — 367 citations into it; #718 of 2,601 by trust here, #738 by citations
Computer Vision and Pattern Recognition — 165 citations into it; #5,061 of 21,575 by trust here, #5,843 by citations
Generative Models — 146 citations into it; #461 of 1,956 by trust here, #717 by citations
Computational Mechanics — 138 citations into it; #3,238 of 10,001 by trust here, #2,309 by citations
Management Science and Operations Research — 79 citations into it; #11,809 of 11,946 by trust here, #8,469 by citations
Safety, Robustness & Interpretability — 76 citations into it
Numerical Analysis — 64 citations into it; #2,406 of 3,586 by trust here, #1,448 by citations

Whom Gauthier Gidel trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

Who trusts Gauthier Gidel overall

Papers from Gauthier Gidel's perspective

Ordered by how strongly the researchers Gauthier Gidel trusts endorse each paper. Same corpus, a different order for every researcher.

Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
ImageNet classification with deep convolutional neural networks
Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
Gradient-based learning applied to document recognition
Long Short-Term Memory
Attention is All you Need (Neural Information Processing Systems)
ImageNet: A large-scale hierarchical image database
Learning Multiple Layers of Features from Tiny Images
Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

A Variational Inequality Perspective on Generative Adversarial Networks (2018) — 409 citations
Implicit Regularization of Discrete Gradient Dynamics in Deep Linear Neural Networks (2019) — 198 citations
Negative Momentum for Improved Game Dynamics (2018) — 192 citations
A Closer Look at the Optimization Landscapes of Generative Adversarial Networks (2019) — 66 citations
Reducing Noise in GAN Training with Variance Reduced Extragradient (2019) — 153 citations
Minimax Theorem for Latent Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets (2020) — 7 citations
Adversarial Example Games (2020) — 57 citations
A Tight and Unified Analysis of Extragradient for a Whole Spectrum of Differentiable Games (2019) — 24 citations
Frank-Wolfe Algorithms for Saddle Point Problems (2016) — 81 citations
Accelerating Smooth Games by Manipulating Spectral Shapes (2020) — 50 citations
Online Adversarial Attacks (2021) — 13 citations
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity (2021) — 64 citations
A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games (2020) — 66 citations
Linear Lower Bounds and Conditioning of Differentiable Games (2019) — 53 citations
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates (2019) — 247 citations
A Variational Inequality Perspective on Generative Adversarial Nets (2018) — 32 citations
Frank-Wolfe Splitting via Augmented Lagrangian Method (2018) — 32 citations
Parametric Adversarial Divergences are Good Task Losses for Generative Modeling (2017) — 13 citations