Samuel F. Dodge

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 5 papers; trusts 727 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 97 citations into it; #4,542 of 21,575 by trust here, #5,680 by citations
• Artificial Intelligence — 59 citations into it; #27,388 of 35,345 by trust here, #19,018 by citations
• Media Technology — 18 citations into it; #412 of 3,621 by trust here, #582 by citations
• Representation & Self-Supervised Learning — 18 citations into it; #2,577 of 3,145 by trust here, #2,217 by citations
• Model Architectures — 14 citations into it
• Optimization & Training — 14 citations into it
• Safety, Robustness & Interpretability — 11 citations into it; #783 of 2,251 by trust here, #1,329 by citations
• Evaluation & Benchmarks — 9 citations into it

Whom Samuel F. Dodge trusts overall

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

1. Karen Simonyan
2. Geoffrey E. Hinton
3. Kaiming He
4. Alex Krizhevsky
5. Christian Szegedy
6. Andrew Zisserman
7. Ross B. Girshick
8. Ian J. Goodfellow
9. Ilya Sutskever
10. Yoshua Bengio

Who trusts Samuel F. Dodge overall

1. Raphael Gontijo Lopes
2. Evgenia Rusak
3. Robert Geirhos
4. Dan Hendrycks
5. Lina Karam
6. Jeremy M. Cohen
7. Igor Vasiljevic
8. Lukas Schott
9. Sylvia J. Smullin
10. Prasun Roy

Papers from Samuel F. Dodge's perspective

Ordered by how strongly the researchers Samuel F. Dodge 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. 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. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• A Study and Comparison of Human and Deep Learning Recognition Performance under Visual Distortions (2017) — 464 citations
• Finding Task-Relevant Features for Few-Shot Learning by Category Traversal (2019) — 381 citations
• Understanding how image quality affects deep neural networks (2016) — 815 citations
• Human and DNN Classification Performance on Images With Quality Distortions (2019) — 40 citations
• Quality Resilient Deep Neural Networks (2017) — 47 citations