Franck Gabriel

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 16 researchers and is trusted by 155 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 62 citations into it; #1,830 of 35,345 by trust here, #3,192 by citations
• Scaling & Learning Theory — 50 citations into it; #22 of 428 by trust here, #56 by citations
• Optimization & Training — 37 citations into it; #2,471 of 2,601 by trust here, #2,378 by citations
• Computer Vision and Pattern Recognition — 28 citations into it; #19,835 of 21,575 by trust here, #10,500 by citations
• Model Architectures — 20 citations into it; #174 of 2,278 by trust here, #393 by citations
• Statistical and Nonlinear Physics — 20 citations into it; #160 of 10,127 by trust here, #292 by citations
• Statistics and Probability — 14 citations into it
• Probabilistic & Bayesian Methods — 7 citations into it

Whom Franck Gabriel 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. Youngmin Cho
8. Alex Krizhevsky
9. Amit Daniely
10. Ilya Sutskever

Who trusts Franck Gabriel 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. Stefano Spigler

Papers from Franck Gabriel's perspective

Ordered by how strongly the researchers Franck Gabriel 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet: A large-scale hierarchical image database
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

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