Lénaïc Chizat
Artificial Intelligence · 2015–2020
A researcher in the Science corpus: 8 papers; trusts 119 researchers and is trusted by 81 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 74 citations into it; #3,172 of 35,345 by trust here, #6,266 by citations
- Applied Mathematics — 45 citations into it; #6,319 of 7,620 by trust here, #4,033 by citations
- Optimization & Training — 45 citations into it; #289 of 2,601 by trust here, #488 by citations
- Computer Vision and Pattern Recognition — 39 citations into it
- Scaling & Learning Theory — 36 citations into it
- Computational Theory and Mathematics — 35 citations into it
- Computational Mechanics — 33 citations into it; #3,360 of 10,001 by trust here, #1,929 by citations
- Model Architectures — 27 citations into it
Whom Lénaïc Chizat trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Cédric Villani
- Francis R. Bach
- Ali Rahimi
- Alex Krizhevsky
- Marco Cuturi
- Simon Shaolei Du
- Song Mei
- Yuanzhi Li
- Kaiming He
- Luigi Ambrosio
Who trusts Lénaïc Chizat overall
- Jason D. Lee
- Atsushi Nitanda
- Yu Bai
- Simon Shaolei Du
- Alberto Bietti
- Song Mei
- Cong Fang
- Sanjeev Arora
- Samet Oymak
- Greg Yang
Papers from Lénaïc Chizat's perspective
Ordered by how strongly the researchers Lénaïc Chizat 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
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- On Lazy Training in Differentiable Programming (2018) — 1,024 citations
- On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport (2018) — 854 citations
- Scaling algorithms for unbalanced optimal transport problems (2017) — 415 citations
- Sample Complexity of Sinkhorn Divergences (2018) — 346 citations
- Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss (2020) — 387 citations
- A Note on Lazy Training in Supervised Differentiable Programming (2018) — 158 citations
- Scaling Algorithms for Unbalanced Transport Problems (2016) — 154 citations
- Unbalanced optimal transport: Dynamic and Kantorovich formulations (2015) — 313 citations