Kevin Yang

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 10 papers; trusts 216 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 143 citations into it; #7,655 of 35,345 by trust here, #10,430 by citations
• Computational Theory and Mathematics — 85 citations into it; #6,698 of 12,182 by trust here, #633 by citations
• Molecular Biology — 73 citations into it
• Computer Vision and Pattern Recognition — 72 citations into it
• Materials Chemistry — 69 citations into it; #4,676 of 11,513 by trust here, #5,426 by citations
• Natural Language Processing — 54 citations into it; #1,684 of 5,688 by trust here, #2,955 by citations
• Computational Chemistry & Drug Discovery — 49 citations into it
• Graph Learning — 43 citations into it; #614 of 693 by trust here, #487 by citations

Whom Kevin Yang trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Alec Radford
5. Alex Krizhevsky
6. Ilya Sutskever
7. Yann LeCun
8. Kaiming He
9. Ronald J. Williams
10. Alex Graves

Who trusts Kevin Yang overall

1. Sachin Kumar
2. Banghao Chen
3. Kushal Arora
4. Fatemehsadat Mireshghallah
5. Michihiro Yasunaga
6. Wengong Jin
7. Charles Burton Snell
8. Hanqing Zhang
9. Xiang Lisa Li
10. Jiawen Deng

Papers from Kevin Yang's perspective

Ordered by how strongly the researchers Kevin Yang 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. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
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

• FUDGE: Controlled Text Generation With Future Discriminators (2021) — 448 citations
• Re3: Generating Longer Stories With Recursive Reprompting and Revision (2022) — 274 citations
• Learning Multimodal Graph-to-Graph Translation for Molecular Optimization (2018) — 267 citations
• Analyzing Learned Molecular Representations for Property Prediction (2019) — 1,807 citations
• A Deep Learning Approach to Antibiotic Discovery. (2020) — 1,250 citations
• A deep learning approach to antibiotic discovery (2020) — 1,050 citations
• Improving Molecular Design by Stochastic Iterative Target Augmentation (2020) — 31 citations
• DOC: Improving Long Story Coherence With Detailed Outline Control (2023) — 141 citations
• Modular Visual Question Answering via Code Generation (2023) — 68 citations
• Uncertainty Quantification Using Neural Networks for Molecular Property Prediction (2020) — 259 citations