Adam W. Harley

Computer Vision and Pattern Recognition · 2015–2023

A researcher in the Science corpus: 7 papers; trusts 1,093 researchers and is trusted by 291 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 188 citations into it; #2,362 of 21,575 by trust here, #4,926 by citations
• Artificial Intelligence — 69 citations into it; #8,318 of 35,345 by trust here, #22,463 by citations
• Evaluation & Benchmarks — 27 citations into it
• Aerospace Engineering — 24 citations into it
• Reinforcement Learning — 24 citations into it
• Control and Systems Engineering — 19 citations into it
• Model Architectures — 17 citations into it
• Natural Language Processing — 15 citations into it

Whom Adam W. Harley trusts overall

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

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

Who trusts Adam W. Harley overall

1. Minsuk Kahng
2. X. Wang
3. David Novotný
4. Shu Kong
5. Christin Seifert
6. Zijie J. Wang
7. Wenguan Wang
8. Daniel Bolya
9. Yiheng Xu
10. C.-C. Jay Kuo

Papers from Adam W. Harley's perspective

Ordered by how strongly the researchers Adam W. Harley 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
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

• Back to Basics: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness (2016) — 434 citations
• Adversarial Inverse Graphics Networks: Learning 2D-to-3D Lifting and Image-to-Image Translation from Unpaired Supervision (2017) — 163 citations
• Segmentation-Aware Convolutional Networks Using Local Attention Masks (2017) — 152 citations
• PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking (2023) — 294 citations
• Reward Learning from Narrated Demonstrations (2018) — 31 citations
• An Interactive Node-Link Visualization of Convolutional Neural Networks (2015) — 126 citations
• Evaluation of deep convolutional nets for document image classification and retrieval (2015) — 468 citations