Chunpeng Wu

Computer Vision and Pattern Recognition · with Chen · 2016–2018

A researcher in the Science corpus: 4 papers; trusts 252 researchers and is trusted by 67 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 73 citations into it; #4,155 of 21,575 by trust here, #2,668 by citations
• Artificial Intelligence — 71 citations into it; #4,926 of 35,345 by trust here, #2,415 by citations
• Optimization & Training — 47 citations into it; #661 of 2,601 by trust here, #241 by citations
• Efficiency & Compression — 38 citations into it; #263 of 1,264 by trust here, #185 by citations
• Model Architectures — 27 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it

Whom Chunpeng Wu trusts overall

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

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

Who trusts Chunpeng Wu overall

1. Wujie Wen
2. Frederick Tung
3. Huan Wang
4. Jaehong Yoon
5. Huanrui Yang
6. Xiaoliang Dai
7. Haichuan Yang
8. Georgios Georgiadis
9. Sara Hooker
10. Sangwon Jung

Papers from Chunpeng Wu's perspective

Ordered by how strongly the researchers Chunpeng Wu 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
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. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Learning Structured Sparsity in Deep Neural Networks (2016) — 2,511 citations
• Coordinating Filters for Faster Deep Neural Networks (2017) — 143 citations
• SmoothOut: Smoothing Out Sharp Minima to Improve Generalization in Deep Learning (2018) — 55 citations
• TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning (2017) — 1,084 citations