Frederic Besse

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 8 papers; trusts 269 researchers and is trusted by 69 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 180 citations into it; #7,427 of 35,345 by trust here, #5,834 by citations
• Computer Vision and Pattern Recognition — 130 citations into it; #3,982 of 21,575 by trust here, #3,426 by citations
• Reinforcement Learning — 112 citations into it; #906 of 2,242 by trust here, #965 by citations
• Generative Models — 64 citations into it; #769 of 1,956 by trust here, #526 by citations
• Model Architectures — 35 citations into it
• Representation & Self-Supervised Learning — 30 citations into it; #1,450 of 3,145 by trust here, #1,246 by citations
• Optimization & Training — 29 citations into it
• Probabilistic & Bayesian Methods — 27 citations into it

Whom Frederic Besse trusts overall

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

1. Alex Graves
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. J. Urgen Schmidhuber
6. Alex Krizhevsky
7. Aäron van den Oord
8. Yann LeCun
9. Ilya Sutskever
10. Richard S. Sutton

Who trusts Frederic Besse overall

1. Danilo Jimenez Rezende
2. Danijar Hafner
3. Jindong Jiang
4. Philip Bachman
5. Diederik P. Kingma
6. Aäron van den Oord
7. Sergey Levine
8. Pieter Abbeel
9. Yoshua Bengio
10. Alex Nichol

Papers from Frederic Besse's perspective

Ordered by how strongly the researchers Frederic Besse 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Neural scene representation and rendering (2018) — 674 citations
• Towards Conceptual Compression (2016) — 258 citations
• Learning and Querying Fast Generative Models for Reinforcement Learning (2018) — 135 citations
• Shaping Belief States with Generative Environment Models for RL (2019) — 124 citations
• Causally Correct Partial Models for Reinforcement Learning (2020) — 36 citations
• Convolution by Evolution: Differentiable Pattern Producing Networks (2016) — 106 citations
• TF-Replicator: Distributed Machine Learning for Researchers (2019) — 20 citations
• Temporal Difference Variational Auto-Encoder (2018) — 132 citations