Dongsheng Li
Artificial Intelligence · Frontiers of Information Technology & Electronic Engineering · 2018–2021
A researcher in the Science corpus: 7 papers; trusts 402 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 237 citations into it; #20,356 of 35,345 by trust here, #8,619 by citations
- Computer Vision and Pattern Recognition — 117 citations into it; #12,830 of 21,575 by trust here, #6,485 by citations
- Natural Language Processing — 86 citations into it; #3,456 of 5,688 by trust here, #2,732 by citations
- Reinforcement Learning — 84 citations into it
- Optimization & Training — 39 citations into it
- Control and Systems Engineering — 20 citations into it
- Management Science and Operations Research — 19 citations into it
- Evaluation & Benchmarks — 18 citations into it
Whom Dongsheng Li trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Alex Krizhevsky
- Diederik P. Kingma
- Ilya Sutskever
- Kaiming He
- Yann LeCun
- Richard S. Sutton
- Alex Graves
- Sergey Levine
Who trusts Dongsheng Li overall
Papers from Dongsheng Li's perspective
Ordered by how strongly the researchers Dongsheng Li trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
Their papers
- Correlation Congruence for Knowledge Distillation (2019) — 645 citations
- Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension (2019) — 68 citations
- A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning (2019) — 98 citations
- Attention-Guided Answer Distillation for Machine Reading Comprehension (2018) — 76 citations
- Decentralized Federated Averaging (2021) — 373 citations
- Deep reinforcement learning: a survey (2020) — 780 citations
- Exploring Pre-trained Language Models for Event Extraction and Generation (2019) — 341 citations