Yitong Li
Artificial Intelligence · with Baldwin · 2017–2019
A researcher in the Science corpus: 3 papers; trusts 60 researchers and is trusted by 16 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 63 citations into it; #10,861 of 35,345 by trust here, #11,921 by citations
- Natural Language Processing — 27 citations into it; #3,140 of 5,688 by trust here, #4,539 by citations
- Computer Vision and Pattern Recognition — 15 citations into it
- Safety, Robustness & Interpretability — 14 citations into it; #1,206 of 2,251 by trust here, #1,945 by citations
- Optimization & Training — 8 citations into it
- Model Architectures — 5 citations into it
- Molecular Biology — 5 citations into it
Whom Yitong Li trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoon Kim
- Dirk Hovy
- Geoffrey E. Hinton
- Sepp Hochreiter
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Alex Krizhevsky
- Yann LeCun
- J. Urgen Schmidhuber
Who trusts Yitong Li overall
- Congzheng Song
- Maximin Coavoux
- Xiang Zhou
- Vitaly Shmatikov
- Ananth Raghunathan
- Mohit Bansal
- Shay B. Cohen
- Dirk Hovy
- Yanai Elazar
- Irena Spasic
Papers from Yitong Li's perspective
Ordered by how strongly the researchers Yitong Li trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- GENERATIVE ADVERSARIAL NETS
Their papers
- Towards Robust and Privacy-preserving Text Representations (2018) — 181 citations
- Robust Training under Linguistic Adversity (2017) — 61 citations
- Towards Fair and Privacy-Preserving Federated Deep Models (2019) — 244 citations