Joseph Paul Cohen

Computer Vision and Pattern Recognition · Artificial Intelligence Review · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 352 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 166 citations into it
• Artificial Intelligence — 111 citations into it; #7,844 of 35,345 by trust here, #10,207 by citations
• Radiology, Nuclear Medicine and Imaging — 56 citations into it; #305 of 3,265 by trust here, #792 by citations
• Optimization & Training — 26 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Model Architectures — 21 citations into it
• Generative Models — 16 citations into it

Whom Joseph Paul Cohen trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Yann LeCun
7. Ross B. Girshick
8. Ilya Sutskever
9. Karen Simonyan
10. Christian Szegedy

Who trusts Joseph Paul Cohen overall

1. Linda Wang
2. Ioannis D. Apostolopoulos
3. Ferhat Ucar
4. Mohammad Rahimzadeh
5. Alex J. DeGrave
6. Asmaa Abbas
7. Ali Narin
8. Md. Zabirul Islam
9. Suat Toraman
10. Mohamed Loey

Papers from Joseph Paul Cohen's perspective

Ordered by how strongly the researchers Joseph Paul Cohen 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Revisiting Training Strategies and Generalization Performance in Deep Metric Learning (2020) — 191 citations
• COVID-19 Image Data Collection: Prospective Predictions Are the Future (2020) — 848 citations
• COVID-19 Image Data Collection (2020) — 1,059 citations
• Deep semantic segmentation of natural and medical images: a review (2019) — 826 citations
• Distribution Matching Losses Can Hallucinate Features in Medical Image Translation (2018) — 352 citations