Jean-François Goudou

Artificial Intelligence · 2018

A researcher in the Science corpus: 2 papers; trusts 208 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it; #19,839 of 35,345 by trust here, #16,166 by citations
• Computer Vision and Pattern Recognition — 42 citations into it; #11,013 of 21,575 by trust here, #19,310 by citations
• Generative Models — 35 citations into it
• Reinforcement Learning — 27 citations into it; #1,433 of 2,242 by trust here, #1,415 by citations
• Representation & Self-Supervised Learning — 20 citations into it; #2,010 of 3,145 by trust here, #1,982 by citations
• Evaluation & Benchmarks — 13 citations into it
• Control and Systems Engineering — 8 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it

Whom Jean-François Goudou trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Yann LeCun
6. Ilya Sutskever
7. Kaiming He
8. Ruslan Salakhutdinov
9. Ian J. Goodfellow
10. Radford M. Neal

Who trusts Jean-François Goudou overall

1. Timothée Lesort
2. Benedikt Bagus
3. Alexander Rainer Tassilo Gepperth
4. Vincenzo Lomonaco
5. Marvin Zhang
6. Hugo Caselles-Dupré
7. Andrei Stoian
8. Brielen Madureira
9. David Filliat

Papers from Jean-François Goudou's perspective

Ordered by how strongly the researchers Jean-François Goudou 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. 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. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• State Representation Learning for Control: An Overview (2018) — 381 citations
• Training Discriminative Models to Evaluate Generative Ones (2018) — 19 citations