Qinyuan Ye

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 8 papers; trusts 556 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 313 citations into it; #18,393 of 35,345 by trust here, #10,667 by citations
• Natural Language Processing — 256 citations into it; #2,603 of 5,688 by trust here, #2,263 by citations
• Computer Vision and Pattern Recognition — 125 citations into it; #18,233 of 21,575 by trust here, #8,952 by citations
• Evaluation & Benchmarks — 78 citations into it; #2,566 of 3,147 by trust here, #1,921 by citations
• Representation & Self-Supervised Learning — 43 citations into it
• Large Language Models — 34 citations into it
• Model Architectures — 19 citations into it
• Prompting & Reasoning — 19 citations into it

Whom Qinyuan Ye trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Ilya Sutskever
5. Alex Krizhevsky
6. Yann LeCun
7. Kaiming He
8. Alex Graves
9. Richard Socher
10. Sepp Hochreiter

Who trusts Qinyuan Ye overall

1. Swaroop Mishra
2. Sewon Min
3. Bill Yuchen Lin
4. Jonathan Bragg
5. Vishakh Padmakumar
6. Mingda Chen
7. Ming Li
8. Chenhao Tan
9. Yujia Qin
10. Sarah Wiegreffe

Papers from Qinyuan Ye's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. Attention is All you Need (Neural Information Processing Systems)
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP (2021) — 202 citations
• Refining Language Models with Compositional Explanations (2021) — 38 citations
• Learning to Generate Task-Specific Adapters from Task Description (2021) — 36 citations
• Learning from Explanations with Neural Execution Tree (2019) — 41 citations
• LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning from Explanation (2020) — 22 citations
• Teaching Machine Comprehension with Compositional Explanations (2020) — 36 citations
• Zero-shot Learning by Generating Task-specific Adapters (2021) — 10 citations
• Learning to Annotate: Modularizing Data Augmentation for Text Classifiers with Natural Language Explanations (2019)