Boyang Deng

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 410 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 145 citations into it; #14,806 of 21,575 by trust here, #7,657 by citations
• Artificial Intelligence — 81 citations into it; #27,493 of 35,345 by trust here, #20,377 by citations
• Computational Mechanics — 53 citations into it; #7,461 of 10,001 by trust here, #2,032 by citations
• Model Architectures — 45 citations into it
• Optimization & Training — 23 citations into it
• Neural Architecture Search & AutoML — 21 citations into it; #215 of 283 by trust here, #241 by citations
• Aerospace Engineering — 15 citations into it
• Geology — 8 citations into it; #629 of 687 by trust here, #499 by citations

Whom Boyang Deng trusts overall

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

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

Who trusts Boyang Deng overall

1. Geoffrey E. Hinton
2. Colin White
3. J. Mellor
4. Yanan Sun
5. Wujie Wen
6. Julien N. Siems
7. Pengzhen Ren
8. Roxana Istrate
9. Songyou Peng
10. Willie Neiswanger

Papers from Boyang Deng's perspective

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

Their papers

• NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis (2020) — 703 citations
• NASA: Neural Articulated Shape Approximation (2019) — 234 citations
• Peephole: Predicting Network Performance Before Training (2017) — 118 citations
• Canonical Capsules: Unsupervised Capsules in Canonical Pose (2020) — 44 citations
• BlockQNN: Efficient Block-Wise Neural Network Architecture Generation (2018) — 137 citations