Irene Y. Chen
Artificial Intelligence · 2018–2021
A researcher in the Science corpus: 5 papers; trusts 29 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 82 citations into it; #20,383 of 35,345 by trust here, #15,004 by citations
- Safety, Robustness & Interpretability — 40 citations into it
- Health Informatics — 26 citations into it; #90 of 240 by trust here, #50 by citations
- Safety Research — 21 citations into it
- Health Information Management — 16 citations into it; #292 of 344 by trust here, #66 by citations
- Epidemiology — 14 citations into it; #760 of 2,148 by trust here, #1,337 by citations
- AI Ethics, Fairness & Society — 13 citations into it; #281 of 494 by trust here, #271 by citations
- Natural Language Processing — 7 citations into it
Whom Irene Y. Chen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Moritz Hardt
- Cynthia Dwork
- Jon M. Kleinberg
- Peter A. Flach
- Yahav Bechavod
- Marzyeh Ghassemi
- Richard S. Zemel
- Faisal Kamiran
- David M. Blei
- Shun-ichi Amari
Who trusts Irene Y. Chen overall
- Silvio Amir
- Shakir Mohamed
- Pang Wei Koh
- Kenneth Holstein
- Berk Ustun
- Marzyeh Ghassemi
- Rajiv Movva
- Jan-Willem van de Meent
- Emma Pierson
- Alexandra Chouldechova
Papers from Irene Y. Chen's perspective
Ordered by how strongly the researchers Irene Y. Chen trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Can AI Help Reduce Disparities in General Medical and Mental Health Care? (2019) — 373 citations
- Ethical Machine Learning in Healthcare (2020) — 577 citations
- Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations (2021) — 830 citations
- A Review of Challenges and Opportunities in Machine Learning for Health. (2018) — 398 citations
- Why Is My Classifier Discriminatory? (2018) — 448 citations