Jérôme Malick

Artificial Intelligence · 2012–2021

A researcher in the Science corpus: 7 papers; trusts 71 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 83 citations into it; #31,035 of 35,345 by trust here, #21,878 by citations
• Optimization & Training — 78 citations into it; #2,401 of 2,601 by trust here, #2,215 by citations
• Computational Mechanics — 57 citations into it; #3,183 of 10,001 by trust here, #3,908 by citations
• Computational Theory and Mathematics — 31 citations into it; #3,805 of 12,182 by trust here, #4,850 by citations
• Numerical Analysis — 25 citations into it; #1,141 of 3,586 by trust here, #1,559 by citations
• Management Science and Operations Research — 19 citations into it
• Mathematical Optimization — 17 citations into it
• Computer Vision and Pattern Recognition — 15 citations into it

Whom Jérôme Malick trusts overall

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

1. Yurii E. Nesterov
2. Peter Richtárik
3. Sébastien Bubeck
4. Gauthier Gidel
5. Arkadi Nemirovski
6. Paul Tseng
7. Simon Lacoste-Julien
8. Fabian Pedregosa
9. Martin Takác
10. Tong Zhang

Who trusts Jérôme Malick overall

1. Breton L. Minnehan
2. Konstantin Mishchenko
3. Nicolas Boumal
4. Jun Li
5. Lei Huang
6. Andreas Savakis
7. Alina Ene

Papers from Jérôme Malick's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. GENERATIVE ADVERSARIAL NETS
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• On the convergence of single-call stochastic extra-gradient methods (2019) — 189 citations
• Projection-like Retractions on Matrix Manifolds (2012) — 376 citations
• Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling (2020) — 80 citations
• A Superquantile Approach to Federated Learning with Heterogeneous Devices (2021) — 28 citations
• A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning (2018) — 40 citations
• Distributed Learning with Sparse Communications by Identification (2018) — 8 citations
• Asynchronous Distributed Learning with Sparse Communications and Identification (2018) — 19 citations