Jean-Luc Gauvain

Natural Language Processing · 1994–2016

A researcher in the Science corpus: 9 papers; trusts 27 researchers and is trusted by 326 researchers here, as inferred from the citation graph.

Subfields

• Natural Language Processing — 30 citations into it; #838 of 5,688 by trust here, #1,221 by citations
• Artificial Intelligence — 22 citations into it; #1,296 of 35,345 by trust here, #3,957 by citations
• Large Language Models — 10 citations into it
• Model Architectures — 7 citations into it; #707 of 2,278 by trust here, #571 by citations
• Probabilistic & Bayesian Methods — 7 citations into it; #197 of 1,525 by trust here, #363 by citations
• Speech & Audio — 7 citations into it; #208 of 1,076 by trust here, #420 by citations
• Signal Processing — 5 citations into it; #620 of 7,572 by trust here, #1,189 by citations

Whom Jean-Luc Gauvain trusts overall

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

1. Alex Graves
2. Geoffrey E. Hinton
3. Chris Leggetter
4. Yoshua Bengio
5. Paul J. Werbos
6. Diederik P. Kingma
7. Patrick Kenny
8. Holger Schwenk
9. J. Urgen Schmidhuber
10. Andriy Mnih

Who trusts Jean-Luc Gauvain overall

1. Chenpeng Du
2. Yoshua Bengio
3. Li Deng
4. Holger Schwenk
5. Susannah Young
6. Patrick Kenny
7. Cees G. M. Snoek
8. Mark John Francis Gales
9. M. A. Carreira-Perpiñán
10. Zhen Huang

Papers from Jean-Luc Gauvain's perspective

Ordered by how strongly the researchers Jean-Luc Gauvain trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Long Short-Term Memory
4. ImageNet classification with deep convolutional neural networks
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Neural Probabilistic Language Models (2006) — 593 citations
• Connectionist language modeling for large vocabulary continuous speech recognition (2002) — 166 citations
• Structured Output Layer neural network language model (2011) — 157 citations
• Lightly supervised and unsupervised acoustic model training (2002) — 307 citations
• Structured Output Layer Neural Network Language Models for Speech Recognition (2013) — 78 citations
• The LIMSI Broadcast News transcription system (2002) — 504 citations
• A Divide-and-Conquer Approach for Language Identification Based on Recurrent Neural Networks (2016) — 28 citations
• Lightly Supervised Acoustic Model Training (2000) — 58 citations
• Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains (1994)