Yifan Peng

Artificial Intelligence · with Lu · 2016–2022

A researcher in the Science corpus: 10 papers; trusts 508 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 322 citations into it; #11,702 of 35,345 by trust here, #4,041 by citations
• Molecular Biology — 301 citations into it; #4,382 of 17,773 by trust here, #3,102 by citations
• Computer Vision and Pattern Recognition — 194 citations into it; #19,622 of 21,575 by trust here, #6,606 by citations
• Natural Language Processing — 56 citations into it; #5,380 of 5,688 by trust here, #3,754 by citations
• Model Architectures — 34 citations into it
• Representation & Self-Supervised Learning — 31 citations into it
• Evaluation & Benchmarks — 30 citations into it; #1,661 of 3,147 by trust here, #666 by citations
• Multimodal Learning — 24 citations into it; #1,076 of 1,211 by trust here, #846 by citations

Whom Yifan Peng trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Ilya Sutskever
6. Robert Leaman
7. Diederik P. Kingma
8. Karen Simonyan
9. Yann LeCun
10. Ross B. Girshick

Who trusts Yifan Peng overall

1. Xiaosong Wang
2. Macarious Abadeer
3. Akshay Smit
4. Pranav Rajpurkar
5. Guanxiong Liu
6. Jeremy Irvin
7. Katikapalli Subramanyam Kalyan
8. Mayur Wankhade
9. Jinghang Gu
10. Zhiyong Lu

Papers from Yifan Peng's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Opportunities and obstacles for deep learning in biology and medicine (2017) — 2,030 citations
• ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases (2019) — 1,263 citations
• TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-Rays (2018) — 560 citations
• NegBio: a high-performance tool for negation and uncertainty detection in radiology reports (2017) — 229 citations
• Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets (2019) — 995 citations
• Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task (2016) — 357 citations
• Improving chemical disease relation extraction with rich features and weakly labeled data (2016) — 81 citations
• Learning to Imagine: Diversify Memory for Incremental Learning using Unlabeled Data (2022) — 37 citations
• MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs (2019) — 1,117 citations
• ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases (2017) — 3,535 citations