Jean-Sébastien Senecal

Natural Language Processing · 2003–2008

A researcher in the Science corpus: 3 papers; trusts 17 researchers and is trusted by 105 researchers here, as inferred from the citation graph.

Subfields

• Natural Language Processing — 25 citations into it; #853 of 5,688 by trust here, #1,466 by citations
• Artificial Intelligence — 20 citations into it; #2,477 of 35,345 by trust here, #6,229 by citations
• Probabilistic & Bayesian Methods — 13 citations into it; #872 of 1,525 by trust here, #1,274 by citations
• Representation & Self-Supervised Learning — 6 citations into it
• Speech & Audio — 5 citations into it

Whom Jean-Sébastien Senecal trusts overall

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

1. Geoffrey E. Hinton
2. Frederick Jelinek
3. Slava M. Katz
4. Reinhard Kneser
5. R. Rosenfeld
6. Yoshua Bengio
7. Stanley F. Chen
8. J. Urgen Schmidhuber
9. Alex Krizhevsky
10. Hermann Ney

Who trusts Jean-Sébastien Senecal overall

1. Andriy Mnih
2. Bryan Perozzi
3. Yoshua Bengio
4. Chris Dyer
5. Rami Al-Rfou
6. Tetiana Parshakova
7. Kyunghyun Cho
8. Xinlei Chen
9. Quoc V. Le
10. Yee Whye Teh

Papers from Jean-Sébastien Senecal's perspective

Ordered by how strongly the researchers Jean-Sébastien Senecal 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. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Neural Probabilistic Language Models (2006) — 593 citations
• Adaptive Importance Sampling to Accelerate Training of a Neural Probabilistic Language Model (2008) — 262 citations
• Quick Training of Probabilistic Neural Nets by Importance Sampling (2003) — 35 citations