Shuyang Cao
Artificial Intelligence · 2021
A researcher in the Science corpus: 2 papers; trusts 180 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 83 citations into it; #25,143 of 35,345 by trust here, #17,776 by citations
- Natural Language Processing — 69 citations into it; #4,168 of 5,688 by trust here, #3,574 by citations
- Computer Vision and Pattern Recognition — 21 citations into it
- Evaluation & Benchmarks — 21 citations into it
- Representation & Self-Supervised Learning — 12 citations into it
- Large Language Models — 10 citations into it
Whom Shuyang Cao trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Alex Krizhevsky
- Kyunghyun Cho
- Kaiming He
- Yann LeCun
- Alex Graves
- Richard Socher
Who trusts Shuyang Cao overall
Papers from Shuyang Cao's perspective
Ordered by how strongly the researchers Shuyang Cao 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)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- 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
- 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
- Efficient Attentions for Long Document Summarization (2021) — 442 citations
- CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization (2021) — 212 citations