Luke Oakden-Rayner

Evaluation & Benchmarks · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Evaluation & Benchmarks — 6 citations into it

Whom Luke Oakden-Rayner trusts overall

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

1. Pranav Rajpurkar
2. Peter J. Rousseeuw
3. Saining Xie
4. Jeremy Irvin
5. Ross B. Girshick
6. Vincent S. Chen
7. Geoffrey E. Hinton
8. Kaiming He
9. Alex Krizhevsky
10. Yann LeCun

Who trusts Luke Oakden-Rayner overall

1. Nimit Sharad Sohoni
2. Jared A. Dunnmon
3. Thomas Liao
4. Shiori Sagawa
5. Alex J. DeGrave

Papers from Luke Oakden-Rayner's perspective

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

Their papers

• Hidden stratification causes clinically meaningful failures in machine learning for medical imaging (2019) — 531 citations
• Detecting hip fractures with radiologist-level performance using deep neural networks (2017) — 115 citations
• Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework (2017) — 149 citations