Emmanuel Bengio

Artificial Intelligence · 2013–2021

A researcher in the Science corpus: 8 papers; trusts 90 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 120 citations into it; #9,137 of 35,345 by trust here, #6,014 by citations
• Reinforcement Learning — 78 citations into it
• Computer Vision and Pattern Recognition — 46 citations into it; #6,411 of 21,575 by trust here, #4,402 by citations
• Optimization & Training — 23 citations into it
• Representation & Self-Supervised Learning — 22 citations into it
• Generative Models — 20 citations into it
• Probabilistic & Bayesian Methods — 20 citations into it
• Management Science and Operations Research — 19 citations into it

Whom Emmanuel Bengio trusts overall

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

1. Richard S. Sutton
2. Ronald J. Williams
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Yann LeCun
7. Diederik P. Kingma
8. Andrew Barto
9. Martin A. Riedmiller
10. Marc G. Bellemare

Who trusts Emmanuel Bengio overall

1. Tom Véniat
2. Rameswar Panda
3. Matthew Riemer
4. Yoshua Bengio
5. Zuxuan Wu
6. Clemens Rosenbaum
7. Bowen Pan
8. Yue Meng
9. Bernhard Schölkopf
10. Ludovic Denoyer

Papers from Emmanuel Bengio's perspective

Ordered by how strongly the researchers Emmanuel Bengio 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Conditional Computation in Neural Networks for faster models (2015) — 375 citations
• Independently Controllable Factors (2017) — 71 citations
• Combining modality specific deep neural networks for emotion recognition in video (2013) — 397 citations
• Independently Controllable Features (2017) — 67 citations
• Interference and Generalization in Temporal Difference Learning (2020) — 69 citations
• Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation (2021) — 514 citations
• GFlowNet Foundations (2021) — 363 citations
• Disentangling the independently controllable factors of variation by interacting with the world (2018) — 69 citations