Nicolas Boulanger-Lewandowski

Artificial Intelligence · 2011–2016

A researcher in the Science corpus: 7 papers; trusts 36 researchers and is trusted by 521 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it; #3,222 of 35,345 by trust here, #8,810 by citations
• Computer Vision and Pattern Recognition — 51 citations into it; #1,965 of 21,575 by trust here, #3,929 by citations
• Model Architectures — 32 citations into it; #276 of 2,278 by trust here, #200 by citations
• Optimization & Training — 27 citations into it; #501 of 2,601 by trust here, #822 by citations
• Representation & Self-Supervised Learning — 24 citations into it; #468 of 3,145 by trust here, #839 by citations
• Probabilistic & Bayesian Methods — 18 citations into it
• Signal Processing — 15 citations into it; #225 of 7,572 by trust here, #410 by citations
• Generative Models — 10 citations into it

Whom Nicolas Boulanger-Lewandowski trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Graves
4. Ilya Sutskever
5. Paul Smolensky
6. Hugo Larochelle
7. James Martens
8. Alex Krizhevsky
9. Max Welling
10. Yann LeCun

Who trusts Nicolas Boulanger-Lewandowski overall

1. Yoshua Bengio
2. Kyunghyun Cho
3. Li Deng
4. Felix Stahlberg
5. Razvan Pascanu
6. Justin Bayer
7. Ruslan Salakhutdinov
8. Zhe Gan
9. Sergey Levine
10. Aaron C. Courville

Papers from Nicolas Boulanger-Lewandowski's perspective

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

Their papers

• Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription (2012) — 739 citations
• Advances in optimizing recurrent networks (2012) — 538 citations
• Combining modality specific deep neural networks for emotion recognition in video (2013) — 397 citations
• EmoNets: Multimodal deep learning approaches for emotion recognition in video (2015) — 412 citations
• Theano: A Python framework for fast computation of mathematical expressions (2016) — 2,380 citations
• Audio Chord Recognition with Recurrent Neural Networks (2013) — 189 citations
• Deep Learners Benefit More from Out-of-Distribution Examples (2011) — 169 citations