Richard Yuanzhe Pang

Artificial Intelligence · 2020–2024

A researcher in the Science corpus: 11 papers; trusts 382 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 319 citations into it; #13,216 of 35,345 by trust here, #5,281 by citations
• Natural Language Processing — 260 citations into it; #5,572 of 5,688 by trust here, #2,385 by citations
• Evaluation & Benchmarks — 128 citations into it
• Computer Vision and Pattern Recognition — 111 citations into it; #11,223 of 21,575 by trust here, #8,409 by citations
• Large Language Models — 53 citations into it
• Model Architectures — 28 citations into it; #2,025 of 2,278 by trust here, #2,009 by citations
• Information Systems — 18 citations into it
• Reinforcement Learning — 18 citations into it; #1,299 of 2,242 by trust here, #1,932 by citations

Whom Richard Yuanzhe Pang 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. Alex Graves
8. Oriol Vinyals
9. Richard S. Sutton
10. Kaiming He

Who trusts Richard Yuanzhe Pang overall

1. Alex Wang
2. Yixin Liu
3. G. Qian
4. Han Guo
5. Kechi Zhang
6. Alicia Parrish
7. Yi Tay
8. Zi-Yi Dou
9. Congbo Ma
10. Caglar Gulcehre

Papers from Richard Yuanzhe Pang's perspective

Ordered by how strongly the researchers Richard Yuanzhe Pang 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. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
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

• Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work? (2020) — 210 citations
• Text Generation by Learning from Demonstrations (2020) — 88 citations
• QuALITY: Question Answering with Long Input Texts, Yes! (2021) — 240 citations
• Consistency of a Recurrent Language Model with Respect to Incomplete Decoding (2020) — 76 citations
• What Do NLP Researchers Believe? Results of the NLP Community Metasurvey (2022) — 48 citations
• GPQA: A Graduate-Level Google-Proof Q&A Benchmark (2023) — 2,996 citations
• SQuALITY: Building a Long-Document Summarization Dataset the Hard Way (2022) — 59 citations
• ENGINE: Energy-Based Inference Networks for Non-Autoregressive Machine Translation (2020) — 58 citations
• Self-Rewarding Language Models (2024) — 663 citations
• AgreeSum: Agreement-Oriented Multi-Document Summarization (2021) — 18 citations
• Comparing Test Sets with Item Response Theory (2021) — 64 citations