Christopher Kermorvant

Artificial Intelligence · 2013–2014

A researcher in the Science corpus: 2 papers; trusts 20 researchers and is trusted by 57 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 17 citations into it; #4,703 of 35,345 by trust here, #10,319 by citations
• Computer Vision and Pattern Recognition — 12 citations into it; #4,098 of 21,575 by trust here, #7,249 by citations
• Model Architectures — 10 citations into it; #439 of 2,278 by trust here, #1,059 by citations
• Natural Language Processing — 8 citations into it
• Signal Processing — 6 citations into it

Whom Christopher Kermorvant trusts overall

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

1. Alex Graves
2. J. Urgen Schmidhuber
3. Geoffrey E. Hinton
4. Urs-Viktor Marti
5. Yoshua Bengio
6. Sepp Hochreiter
7. Alex Krizhevsky
8. Li Deng
9. Yann LeCun
10. Ilya Sutskever

Who trusts Christopher Kermorvant overall

1. Yoav Goldberg
2. Yarin Gal
3. Shubham Toshniwal
4. Graham Neubig
5. Yunseok Jang
6. Timothy Baldwin
7. Wojciech Zaremba
8. Minh-Thang Luong
9. Ilya Sutskever
10. Jason P. C. Chiu

Papers from Christopher Kermorvant's perspective

Ordered by how strongly the researchers Christopher Kermorvant trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Long Short-Term Memory
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Dropout Improves Recurrent Neural Networks for Handwriting Recognition (2013) — 587 citations
• The A2iA Arabic Handwritten Text Recognition System at the Open HaRT2013 Evaluation (2014) — 48 citations