Alvin Rajkomar

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 5 papers; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 118 citations into it
• Health Information Management — 24 citations into it
• Radiology, Nuclear Medicine and Imaging — 22 citations into it
• Epidemiology — 21 citations into it
• Cardiology and Cardiovascular Medicine — 10 citations into it
• Genetics — 6 citations into it

Whom Alvin Rajkomar trusts overall

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

1. Geoffrey E. Hinton
2. Nancy R. Cook
3. Yoshua Bengio
4. Alex Krizhevsky
5. Andrew Beam
6. Christian Szegedy
7. Sepp Hochreiter
8. Alex Smola
9. Ilya Sutskever
10. Dzmitry Bahdanau

Who trusts Alvin Rajkomar overall

1. Jeremy Goecks
2. Xinggang Wang
3. Trishan Panch
4. Edmon Begoli
5. Danton S. Char
6. Marzyeh Ghassemi
7. Michael W. Dusenberry
8. Hrayr Harutyunyan
9. Julius Adebayo
10. Zeeshan Ahmed

Papers from Alvin Rajkomar's perspective

Ordered by how strongly the researchers Alvin Rajkomar 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. Long Short-Term Memory
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Going deeper with convolutions
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Large language models encode clinical knowledge (2022) — 4,703 citations
• Scalable and accurate deep learning with electronic health records (2018) — 2,800 citations
• High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks (2016) — 137 citations
• Machine Learning in Medicine (2019) — 1,711 citations
• Ensuring Fairness in Machine Learning to Advance Health Equity (2018) — 983 citations