Joshua C. Peterson

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 2 papers; trusts 231 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 36 citations into it; #31,178 of 35,345 by trust here, #21,316 by citations
• Computer Vision and Pattern Recognition — 33 citations into it
• Model Architectures — 11 citations into it
• Safety, Robustness & Interpretability — 9 citations into it; #1,973 of 2,251 by trust here, #2,041 by citations
• Evaluation & Benchmarks — 6 citations into it
• Optimization & Training — 6 citations into it
• Signal Processing — 5 citations into it

Whom Joshua C. Peterson trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Karen Simonyan
4. Christian Szegedy
5. Geoffrey E. Hinton
6. Antonio Torralba
7. Diederik P. Kingma
8. Yoshua Bengio
9. Andrew Zisserman
10. Ian J. Goodfellow

Who trusts Joshua C. Peterson overall

1. Hussein Mozannar
2. Drew Linsley
3. Ethan Harris
4. Vamsi Krishna Ithapu
5. Zhilu Zhang
6. David A. Sontag
7. Sven Eberhardt

Papers from Joshua C. Peterson's perspective

Ordered by how strongly the researchers Joshua C. Peterson 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Human Uncertainty Makes Classification More Robust (2019) — 386 citations
• Adapting Deep Network Features to Capture Psychological Representations (2016) — 72 citations