Min Wang

Computer Vision and Pattern Recognition · with Liu · 2015–2022

A researcher in the Science corpus: 5 papers; trusts 314 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 48 citations into it; #7,284 of 21,575 by trust here, #5,116 by citations
• Artificial Intelligence — 41 citations into it
• Model Architectures — 27 citations into it; #729 of 2,278 by trust here, #600 by citations
• Efficiency & Compression — 22 citations into it; #354 of 1,264 by trust here, #360 by citations
• Computational Mechanics — 13 citations into it; #2,680 of 10,001 by trust here, #962 by citations
• Optimization & Training — 12 citations into it

Whom Min Wang trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Christian Szegedy
4. Geoffrey E. Hinton
5. Sergey Ioffe
6. X. Zhang
7. Ilya Sutskever
8. Karen Simonyan
9. Jia Deng
10. Yann LeCun

Who trusts Min Wang overall

1. Liang-Chieh Chen
2. Wujie Wen
3. Rahaf Aljundi
4. José Manuel Álvarez
5. Weiyang Liu
6. Timur Garipov
7. Yawei Li
8. Haichuan Yang
9. François Chollet
10. Yukun Zhu

Papers from Min Wang's perspective

Ordered by how strongly the researchers Min 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. 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. Long Short-Term Memory
8. Going deeper with convolutions
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Sparse Convolutional Neural Networks (2015) — 924 citations
• Factorized Convolutional Neural Networks (2016) — 188 citations
• Deep Multiscale Model Learning (2018) — 84 citations
• Design of Efficient Convolutional Layers using Single Intra-channel Convolution, Topological Subdivisioning and Spatial "Bottleneck" Structure (2016) — 53 citations
• Identification of antimicrobial peptides from the human gut microbiome using deep learning (2022) — 388 citations