Nicholas Léonard

Artificial Intelligence · 2013–2016

A researcher in the Science corpus: 4 papers; trusts 37 researchers and is trusted by 532 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 43 citations into it; #775 of 35,345 by trust here, #2,522 by citations
• Computer Vision and Pattern Recognition — 31 citations into it; #12,745 of 21,575 by trust here, #15,529 by citations
• Optimization & Training — 29 citations into it
• Model Architectures — 22 citations into it; #131 of 2,278 by trust here, #146 by citations
• Reinforcement Learning — 8 citations into it
• Representation & Self-Supervised Learning — 7 citations into it
• Management Science and Operations Research — 6 citations into it
• Signal Processing — 5 citations into it; #4,210 of 7,572 by trust here, #4,560 by citations

Whom Nicholas Léonard trusts overall

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

1. Geoffrey E. Hinton
2. Ronald J. Williams
3. Yoshua Bengio
4. Alex Graves
5. Ilya Sutskever
6. Ronan Collobert
7. Alex Krizhevsky
8. Sepp Hochreiter
9. Yann LeCun
10. David E. Rumelhart

Who trusts Nicholas Léonard overall

1. Yoshua Bengio
2. Danijar Hafner
3. Kyunghyun Cho
4. Markus Nagel
5. Nicholas Carlini
6. Sergey Levine
7. Arash Vahdat
8. Ji Lin
9. Rameswar Panda
10. Yarin Gal

Papers from Nicholas Léonard's perspective

Ordered by how strongly the researchers Nicholas Léonard 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. 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. Going deeper with convolutions
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation (2013) — 4,047 citations
• rnn : Recurrent Library for Torch (2015) — 43 citations
• Deep Q-Networks for Accelerating the Training of Deep Neural Networks (2016) — 12 citations
• Theano: A Python framework for fast computation of mathematical expressions (2016) — 2,380 citations