Laurent Sartran
Artificial Intelligence · with Dyer · 2019–2022
A researcher in the Science corpus: 3 papers; trusts 83 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 133 citations into it
- Natural Language Processing — 97 citations into it
- Computer Vision and Pattern Recognition — 76 citations into it
- Generative Models — 30 citations into it
- Statistical and Nonlinear Physics — 13 citations into it
- Evaluation & Benchmarks — 10 citations into it
- Machine Translation — 5 citations into it
- Management Science and Operations Research — 5 citations into it
Whom Laurent Sartran trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Ilya Sutskever
- Alex Krizhevsky
- Jimmy Ba
- Rico Sennrich
- Yann LeCun
- Kaiming He
- Alex Graves
Who trusts Laurent Sartran overall
Papers from Laurent Sartran's perspective
Ordered by how strongly the researchers Laurent Sartran trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- Continuous diffusion for categorical data (2022) — 209 citations
- Putting Machine Translation in Context with the Noisy Channel Model (2019) — 7 citations
- Better Document-Level Machine Translation with Bayes’ Rule (2019) — 48 citations