Nathan Paczan

Computer Vision and Pattern Recognition · 2020

A researcher in the Science corpus: 1 paper; trusts 480 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 74 citations into it; #9,512 of 21,575 by trust here, #12,992 by citations
• Aerospace Engineering — 28 citations into it
• 3D Vision & Pose — 27 citations into it
• Evaluation & Benchmarks — 26 citations into it; #1,424 of 3,147 by trust here, #2,330 by citations
• Artificial Intelligence — 15 citations into it
• Computer Graphics and Computer-Aided Design — 11 citations into it
• Geology — 11 citations into it; #307 of 687 by trust here, #392 by citations
• Computational Mechanics — 9 citations into it

Whom Nathan Paczan trusts overall

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

1. Kaiming He
2. Jitendra Malik
3. Geoffrey E. Hinton
4. Ross B. Girshick
5. Alex Krizhevsky
6. Yann LeCun
7. Ankur Handa
8. Diederik P. Kingma
9. Yoshua Bengio
10. Karen Simonyan

Who trusts Nathan Paczan overall

1. Roman Bachmann
2. Lihe Yang
3. Ainaz Eftekhar
4. Chaozheng Wu
5. Shariq Farooq Bhat
6. An-Chieh Cheng
7. Xiao Fu

Papers from Nathan Paczan's perspective

Ordered by how strongly the researchers Nathan Paczan 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. Attention is All you Need (Neural Information Processing Systems)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding (2020) — 719 citations