Z. Li

Computer Vision and Pattern Recognition · 2011–2019

A researcher in the Science corpus: 15 papers; trusts 992 researchers and is trusted by 49 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 315 citations into it; #7,303 of 21,575 by trust here, #3,909 by citations
• Artificial Intelligence — 187 citations into it; #17,242 of 35,345 by trust here, #13,760 by citations
• Molecular Biology — 87 citations into it; #1,871 of 17,773 by trust here, #3,737 by citations
• Computational Biology & Bioinformatics — 60 citations into it; #199 of 660 by trust here, #240 by citations
• Model Architectures — 60 citations into it
• Media Technology — 44 citations into it
• Signal Processing — 41 citations into it; #7,434 of 7,572 by trust here, #4,373 by citations
• Optimization & Training — 34 citations into it

Whom Z. Li trusts overall

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

1. Yoshua Bengio
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Kaiming He
6. David G. Lowe
7. Yann LeCun
8. Ilya Sutskever
9. Jimmy Ba
10. Ross B. Girshick

Who trusts Z. Li overall

1. Shengcai Liao
2. Yi Li
3. Michael Fu
4. Sheng Wang
5. Akosua Busia
6. Saikat Chakraborty
7. John Lambert
8. Liang Zheng
9. Yizheng Chen
10. Guanjun Lin

Papers from Z. Li's perspective

Ordered by how strongly the researchers Z. Li 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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

• Learning Locally-Adaptive Decision Functions for Person Verification (2013) — 528 citations
• High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference (2017) — 324 citations
• Feedback Network for Image Super-Resolution (2019) — 821 citations
• Graph-RISE: Graph-Regularized Image Semantic Embedding (2019) — 43 citations
• Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model (2016) — 873 citations
• Towards Better Analysis of Deep Convolutional Neural Networks (2016) — 514 citations
• Understanding Hidden Memories of Recurrent Neural Networks (2017) — 223 citations
• Predicting membrane protein contacts from non-membrane proteins by deep transfer learning (2017) — 5 citations
• Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Networks (2016) — 121 citations
• SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities (2018) — 781 citations
• A Security Analysis of Honeywords (2018) — 1,132 citations
• A Review on Human Activity Recognition Using Vision-Based Method (2017) — 306 citations
• Knowledge Concentration: Learning 100K Object Classifiers in a Single CNN (2017) — 20 citations
• Blockout: Dynamic Model Selection for Hierarchical Deep Networks (2015) — 52 citations
• Learning to Search Efficiently in High Dimensions (2011) — 23 citations