Yannick Estève
Artificial Intelligence · 2012–2020
A researcher in the Science corpus: 4 papers; trusts 30 researchers and is trusted by 29 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 40 citations into it; #7,672 of 35,345 by trust here, #9,170 by citations
- Speech & Audio — 25 citations into it; #331 of 1,076 by trust here, #379 by citations
- Signal Processing — 24 citations into it; #5,490 of 7,572 by trust here, #3,428 by citations
- Natural Language Processing — 12 citations into it; #3,366 of 5,688 by trust here, #4,250 by citations
- Representation & Self-Supervised Learning — 10 citations into it
- Machine Translation — 8 citations into it
- Model Architectures — 7 citations into it
- Computer Vision and Pattern Recognition — 5 citations into it
Whom Yannick Estève trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Daniel Povey
- Andreas Stolcke
- Anthony Rousseau
- Sylvain Meignier
- Brian Kingsbury
- Vijayaditya Peddinti
- Alex Graves
- Yoshua Bengio
- Frantisek Grézl
- Robert C. Moore
Who trusts Yannick Estève overall
- Kyu J. Han
- Yu Zhang
- François Hernandez
- Chengyi Wang
- Shigeki Karita
- Matthias Sperber
- Tatiana Likhomanenko
Papers from Yannick Estève's perspective
Ordered by how strongly the researchers Yannick Estève trusts endorse each paper. Same corpus, a different order for every researcher.
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Long Short-Term Memory
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- The Kaldi Speech Recognition Toolkit
Their papers
- TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation (2018) — 434 citations
- TED-LIUM: an Automatic Speech Recognition dedicated corpus (2012) — 322 citations
- Enhancing the TED-LIUM Corpus with Selected Data for Language Modeling and More TED Talks (2014) — 304 citations
- Investigating Self-Supervised Pre-Training for End-to-End Speech Translation (2020) — 40 citations