Jean-Pierre David

Computer Vision and Pattern Recognition · 2014–2015

A researcher in the Science corpus: 4 papers; trusts 119 researchers and is trusted by 459 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 35 citations into it; #2,036 of 21,575 by trust here, #2,316 by citations
• Artificial Intelligence — 32 citations into it; #1,808 of 35,345 by trust here, #3,059 by citations
• Model Architectures — 23 citations into it
• Optimization & Training — 18 citations into it
• ML Systems & Hardware — 13 citations into it
• Efficiency & Compression — 9 citations into it; #33 of 1,264 by trust here, #103 by citations
• Hardware and Architecture — 9 citations into it
• Computational Theory and Mathematics — 7 citations into it; #4,167 of 12,182 by trust here, #7,851 by citations

Whom Jean-Pierre David trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Yann LeCun
5. Ian J. Goodfellow
6. Ilya Sutskever
7. Diederik P. Kingma
8. Xavier Glorot
9. Matthew D. Zeiler
10. L. Eon Bottou

Who trusts Jean-Pierre David overall

1. Tim Dettmers
2. Itay Hubara
3. Philipp Gysel
4. James T. Wilson
5. Matthieu Courbariaux
6. Yoshua Bengio
7. Karim Ahmed
8. Han Cai
9. Patrick Judd
10. Linfeng Zhang

Papers from Jean-Pierre David's perspective

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

Their papers

• BinaryConnect: Training Deep Neural Networks with binary weights during propagations (2015) — 3,245 citations
• Low precision storage for deep learning (2014) — 51 citations
• Training deep neural networks with low precision multiplications (2014) — 607 citations
• Low precision arithmetic for deep learning (2014) — 175 citations