Yicheng Fan
Artificial Intelligence · 2022
A researcher in the Science corpus: 2 papers; trusts 19 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 106 citations into it
- Natural Language Processing — 80 citations into it
- Evaluation & Benchmarks — 29 citations into it
Whom Yicheng Fan trusts
Their strongest outbound trust, strongest first, inferred from their citations.
- Christopher D. Manning (in Artificial Intelligence)
- Samuel R. Bowman (in Artificial Intelligence)
- Yoshua Bengio (in Artificial Intelligence)
- Diederik P. Kingma (in Artificial Intelligence)
- Quoc V. Le (in Artificial Intelligence)
- Andreas Vlachos (in Artificial Intelligence)
- Ilya Sutskever (in Artificial Intelligence)
- Jimmy Ba (in Artificial Intelligence)
- Richard Socher (in Artificial Intelligence)
- James Thorne (in Artificial Intelligence)
Papers from Yicheng Fan's perspective
Ordered by how strongly the researchers Yicheng Fan trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (Journal of machine learning research)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Language Models are Few-Shot Learners (Neural Information Processing Systems)
- ImageNet classification with deep convolutional neural networks
- 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
- Attributed Text Generation via Post-hoc Research and Revision (2022) — 27 citations
- RARR: Researching and Revising What Language Models Say, Using Language Models (2022) — 339 citations