Ke Tang

Artificial Intelligence · 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) · 2008–2020

A researcher in the Science corpus: 12 papers; trusts 547 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 185 citations into it; #4,853 of 35,345 by trust here, #10,065 by citations
• Computer Vision and Pattern Recognition — 108 citations into it
• Computational Theory and Mathematics — 88 citations into it; #870 of 12,182 by trust here, #1,557 by citations
• Safety, Robustness & Interpretability — 60 citations into it
• Optimization & Training — 28 citations into it; #2,598 of 2,601 by trust here, #2,378 by citations
• Management Science and Operations Research — 25 citations into it
• Model Architectures — 20 citations into it
• Computational Mechanics — 16 citations into it

Whom Ke Tang trusts overall

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

1. Mitchell A. Potter
2. Ross B. Girshick
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Thomas Bäck
6. Xin Yao
7. Martin Pelikan
8. Kalyanmoy Deb
9. Kaiming He
10. Karen Simonyan

Who trusts Ke Tang overall

1. Ran Cheng
2. Mohammad Nabi Omidvar
3. Yaochu Jin
4. Swagatam Das
5. Sedigheh Mahdavi
6. Ponnuthurai Nagaratnam Suganthan
7. Xiaodong Li
8. Hamid Salimi
9. Xin Yao
10. Ye Tian

Papers from Ke Tang's perspective

Ordered by how strongly the researchers Ke Tang 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
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

• Many-Objective Evolutionary Algorithms (2015) — 509 citations
• Large scale evolutionary optimization using cooperative coevolution (2008) — 1,038 citations
• Multilevel cooperative coevolution for large scale optimization (2008) — 354 citations
• Self-adaptive differential evolution with neighborhood search (2008) — 380 citations
• Transfer Learning for Drug Discovery. (2020) — 307 citations
• A Survey on Neural Network Interpretability (2020) — 932 citations
• Stochastic Gradient Descent for Nonconvex Learning Without Bounded Gradient Assumptions (2019) — 144 citations
• Efficient DNN Neuron Pruning by Minimizing Layer-wise Nonlinear Reconstruction Error (2018) — 47 citations
• Relief R-CNN: Utilizing Convolutional Features for Fast Object Detection (2016) — 7 citations
• Relief Impression Image Detection : Unsupervised Extracting Objects Directly From Feature Arrangements of Deep CNN (2016)
• Benchmark Functions for the CEC'2010 Special Session and Competition on Large-Scale (2009) — 523 citations
• Benchmark Functions for the CEC'2013 Special Session and Competition on Large-Scale Global Optimization (2008) — 893 citations