Yandan Wang

Computer Vision and Pattern Recognition · 2014–2018

A researcher in the Science corpus: 6 papers; trusts 310 researchers and is trusted by 67 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 90 citations into it; #4,154 of 21,575 by trust here, #2,625 by citations
• Artificial Intelligence — 84 citations into it; #4,927 of 35,345 by trust here, #2,415 by citations
• Optimization & Training — 62 citations into it; #662 of 2,601 by trust here, #241 by citations
• Efficiency & Compression — 39 citations into it; #264 of 1,264 by trust here, #185 by citations
• Model Architectures — 28 citations into it
• Scaling & Learning Theory — 17 citations into it
• Probabilistic & Bayesian Methods — 8 citations into it
• Statistical and Nonlinear Physics — 5 citations into it

Whom Yandan Wang trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yann LeCun
4. Kaiming He
5. L. Eon Bottou
6. Diederik P. Kingma
7. Yoshua Bengio
8. Ilya Sutskever
9. Radford M. Neal
10. Max Welling

Who trusts Yandan Wang overall

1. Wujie Wen
2. Frederick Tung
3. Huan Wang
4. Xianye Ben
5. Huanrui Yang
6. Jaehong Yoon
7. Xiaoliang Dai
8. Haichuan Yang
9. Georgios Georgiadis
10. By Lei Deng

Papers from Yandan Wang's perspective

Ordered by how strongly the researchers Yandan Wang 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. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

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
• SmoothOut: Smoothing Out Sharp Minima for Generalization in Large-Batch Deep Learning (2018) — 8 citations
• LBP with Six Intersection Points: Reducing Redundant Information in LBP-TOP for Micro-expression Recognition (2014) — 316 citations