Grégoire Mesnil

Artificial Intelligence · 2011–2015

A researcher in the Science corpus: 8 papers; trusts 131 researchers and is trusted by 963 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 90 citations into it; #1,165 of 35,345 by trust here, #2,425 by citations
• Natural Language Processing — 60 citations into it; #879 of 5,688 by trust here, #836 by citations
• Computer Vision and Pattern Recognition — 30 citations into it; #1,526 of 21,575 by trust here, #3,251 by citations
• Representation & Self-Supervised Learning — 30 citations into it; #1,158 of 3,145 by trust here, #736 by citations
• Signal Processing — 19 citations into it; #4,852 of 7,572 by trust here, #2,276 by citations
• Model Architectures — 18 citations into it
• Probabilistic & Bayesian Methods — 17 citations into it; #521 of 1,525 by trust here, #363 by citations
• Information Retrieval & Search — 14 citations into it; #367 of 1,022 by trust here, #277 by citations

Whom Grégoire Mesnil trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Tomas Mikolov
4. Ruslan Salakhutdinov
5. Yann LeCun
6. Alex Krizhevsky
7. Radford M. Neal
8. Ilya Sutskever
9. Li Deng
10. Ronan Collobert

Who trusts Grégoire Mesnil overall

1. Yoshua Bengio
2. Kyunghyun Cho
3. Li Deng
4. Jianfeng Gao
5. Bing Liu
6. Aaron C. Courville
7. Xiaodong He
8. Zhe Gan
9. Sergey Levine
10. Mohit Bansal

Papers from Grégoire Mesnil's perspective

Ordered by how strongly the researchers Grégoire Mesnil 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Better Mixing via Deep Representations (2012) — 356 citations
• A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval (2014) — 727 citations
• Higher Order Contractive Auto-Encoder (2011) — 283 citations
• Investigation of recurrent-neural-network architectures and learning methods for spoken language understanding (2013) — 444 citations
• Learning semantic representations using convolutional neural networks for web search (2014) — 726 citations
• Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding (2015) — 587 citations
• Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews (2014) — 145 citations
• Unsupervised and Transfer Learning Challenge: a Deep Learning Approach (2011) — 241 citations