Yicheng Fu

Artificial Intelligence · 2021–2022

A researcher in the Science corpus: 2 papers; trusts 16 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 47 citations into it
• Natural Language Processing — 33 citations into it
• Evaluation & Benchmarks — 20 citations into it

Whom Yicheng Fu trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Xavier Carreras
2. Xiang Lisa Li
3. Brian Lester
4. Percy Liang
5. Pranav Rajpurkar
6. Geoffrey E. Hinton
7. Timo Schick
8. Yoshua Bengio
9. Diederik P. Kingma
10. Guanghui Qin

Papers from Yicheng Fu's perspective

Ordered by how strongly the researchers Yicheng Fu trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Language Models are Unsupervised Multitask Learners
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (Journal of machine learning research)
9. ImageNet: A large-scale hierarchical image database
10. Gradient-based learning applied to document recognition

Their papers

• P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks (2021) — 1,125 citations
• P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks (2022) — 1,117 citations