Clément Hongler

Artificial Intelligence · 2012–2019

A researcher in the Science corpus: 5 papers; trusts 20 researchers and is trusted by 158 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 63 citations into it; #1,829 of 35,345 by trust here, #3,192 by citations
• Scaling & Learning Theory — 50 citations into it; #21 of 428 by trust here, #56 by citations
• Optimization & Training — 37 citations into it; #2,470 of 2,601 by trust here, #2,378 by citations
• Computer Vision and Pattern Recognition — 28 citations into it; #19,834 of 21,575 by trust here, #10,500 by citations
• Statistics and Probability — 23 citations into it; #10,305 of 12,932 by trust here, #8,740 by citations
• Model Architectures — 20 citations into it; #173 of 2,278 by trust here, #393 by citations
• Statistical and Nonlinear Physics — 20 citations into it; #159 of 10,127 by trust here, #292 by citations
• Mathematical Physics — 13 citations into it; #7,026 of 8,562 by trust here, #5,408 by citations

Whom Clément Hongler trusts overall

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

1. Stanislav Smirnov
2. Dmitry Chelkak
3. Radford M. Neal
4. Gregory F. Lawler
5. Xavier Glorot
6. Yoshua Bengio
7. Geoffrey E. Hinton
8. Youngmin Cho
9. Yann LeCun
10. Diederik P. Kingma

Who trusts Clément Hongler 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 Clément Hongler's perspective

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

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

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
• Conformal invariance of spin correlations in the planar Ising model (2012) — 187 citations
• Convergence of Ising interfaces to Schramm's SLE curves (2013) — 216 citations