Edward James Smith

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 517 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 89 citations into it; #7,099 of 21,575 by trust here, #8,296 by citations
• Computational Mechanics — 41 citations into it; #4,638 of 10,001 by trust here, #2,361 by citations
• Generative Models — 27 citations into it
• 3D Vision & Pose — 23 citations into it; #381 of 689 by trust here, #421 by citations
• Computer Graphics and Computer-Aided Design — 20 citations into it
• Aerospace Engineering — 16 citations into it
• Geology — 12 citations into it
• Artificial Intelligence — 10 citations into it

Whom Edward James Smith trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Kaiming He
4. Alex Krizhevsky
5. Yoshua Bengio
6. Yann LeCun
7. Jimmy Ba
8. Karen Simonyan
9. Individualized Treat
10. Ross B. Girshick

Who trusts Edward James Smith overall

1. Xian-Feng Han
2. Georgia Gkioxari
3. Jun Gao
4. Julian Chibane
5. Saeed Anwar
6. Wentao Yuan
7. Yaxing Wang
8. Lars M. Mescheder
9. Jiahao Li
10. Hamid Laga

Papers from Edward James Smith's perspective

Ordered by how strongly the researchers Edward James Smith 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. 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

• Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer (2019) — 398 citations
• Improved Adversarial Systems for 3D Object Generation and Reconstruction (2017) — 187 citations
• Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation (2018) — 56 citations