Deming Ye
Artificial Intelligence · 2019–2020
A researcher in the Science corpus: 3 papers; trusts 45 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 121 citations into it; #31,613 of 35,345 by trust here, #12,761 by citations
- Natural Language Processing — 97 citations into it; #5,450 of 5,688 by trust here, #4,601 by citations
Whom Deming Ye trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Geoffrey E. Hinton
- Yoshua Bengio
- Sepp Hochreiter
- Richard Socher
- Ilya Sutskever
- Kenton C. T. Lee
- Alex Krizhevsky
- J. Urgen Schmidhuber
- Jimmy Ba
Who trusts Deming Ye overall
- Wenxuan Zhou
- Yuval Varkel
- Ori Ram
- Amrith Krishna
- Zhenghao Liu
- Yujia Qin
- Amir Globerson
- Prakhar Gupta
- Hanmeng Liu
- Yankai Lin
Papers from Deming Ye's perspective
Ordered by how strongly the researchers Deming Ye trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet classification with deep convolutional neural networks
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Coreferential Reasoning Learning for Language Representation (2020) — 191 citations
- CPM: A Large-scale Generative Chinese Pre-trained Language Model (2020) — 132 citations
- DocRED: A Large-Scale Document-Level Relation Extraction Dataset (2019) — 584 citations