Brandon M. Greenwell
Artificial Intelligence · 2017–2019
A researcher in the Science corpus: 3 papers; trusts 32 researchers and is trusted by 46 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 63 citations into it; #4,184 of 35,345 by trust here, #8,210 by citations
- Safety, Robustness & Interpretability — 43 citations into it; #339 of 2,251 by trust here, #748 by citations
- Computer Vision and Pattern Recognition — 22 citations into it; #7,681 of 21,575 by trust here, #19,766 by citations
- Safety Research — 20 citations into it
- AI Ethics, Fairness & Society — 14 citations into it
- Statistics and Probability — 9 citations into it
- Health Informatics — 7 citations into it
Whom Brandon M. Greenwell trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- John Fox
- Deepayan Sarkar
- Jerome H. Friedman
- Andy Liaw
- Cynthia Rudin
- Zachary Chase Lipton
- Matthew C. Wiener
- Bogdan E. Popescu
- Sandra Wachter
- Geoffrey E. Hinton
Who trusts Brandon M. Greenwell overall
- Przemyslaw Biecek
- Christoph Molnar
- Mateusz Staniak
- Giuseppe Casalicchio
- Enrico Camporeale
- Shubham Rathi
- Bernd Bischl
- Arun Das
- Karthikeyan K
- Maximilian Pichler
Papers from Brandon M. Greenwell's perspective
Ordered by how strongly the researchers Brandon M. Greenwell 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Interpretable Machine Learning (2019) — 3,181 citations
- Hands-On Machine Learning with R (2019) — 474 citations
- pdp: An R Package for Constructing Partial Dependence Plots (2017) — 955 citations