Arthur Jacot

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 5 papers; trusts 47 researchers and is trusted by 155 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 98 citations into it; #1,837 of 35,345 by trust here, #3,155 by citations
• Scaling & Learning Theory — 83 citations into it; #24 of 428 by trust here, #56 by citations
• Optimization & Training — 52 citations into it; #2,469 of 2,601 by trust here, #2,162 by citations
• Computer Vision and Pattern Recognition — 40 citations into it; #19,836 of 21,575 by trust here, #10,500 by citations
• Statistical and Nonlinear Physics — 34 citations into it; #161 of 10,127 by trust here, #284 by citations
• Model Architectures — 24 citations into it; #172 of 2,278 by trust here, #389 by citations
• Statistics and Probability — 21 citations into it
• Probabilistic & Bayesian Methods — 13 citations into it

Whom Arthur Jacot trusts overall

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

1. Radford M. Neal
2. Yoshua Bengio
3. Xavier Glorot
4. Geoffrey E. Hinton
5. Yann LeCun
6. Diederik P. Kingma
7. Alex Krizhevsky
8. Youngmin Cho
9. Ilya Sutskever
10. Kaiming He

Who trusts Arthur Jacot overall

1. Greg Yang
2. Zeyuan Allen-Zhu
3. Simon Shaolei Du
4. Mario Geiger
5. Daniel S. Park
6. Yuanzhi Li
7. Sanjeev Arora
8. James Martens
9. Soufiane Hayou
10. Lechao Xiao

Papers from Arthur Jacot's perspective

Ordered by how strongly the researchers Arthur Jacot 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Neural Tangent Kernel: Convergence and Generalization in Neural Networks (2018) — 4,156 citations
• Scaling description of generalization with number of parameters in deep learning (2019) — 214 citations
• Freeze and Chaos for DNNs: an NTK view of Batch Normalization, Checkerboard and Boundary Effects (2019) — 25 citations
• Disentangling feature and lazy training in deep neural networks (2019) — 152 citations
• Disentangling feature and lazy learning in deep neural networks: an empirical study (2019) — 17 citations