Handing Wang

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 8 papers; trusts 122 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 170 citations into it; #13,943 of 35,345 by trust here, #20,377 by citations
• Computational Theory and Mathematics — 130 citations into it; #1,970 of 12,182 by trust here, #2,419 by citations
• Management Science and Operations Research — 36 citations into it
• Computer Vision and Pattern Recognition — 25 citations into it
• Model Architectures — 18 citations into it
• Mechanical Engineering — 15 citations into it
• Optimization & Training — 14 citations into it
• Control and Systems Engineering — 12 citations into it

Whom Handing Wang trusts overall

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

1. Yaochu Jin
2. Qingfu Zhang
3. Kalyanmoy Deb
4. Tinkle Chugh
5. Eckart Zitzler
6. Michael L. Stein
7. Kaisa Miettinen
8. Leo Breiman
9. Joshua D. Knowles
10. Peter John Fleming

Who trusts Handing Wang overall

1. Yaochu Jin
2. Yanan Sun
3. Tinkle Chugh
4. Shouyong Jiang
5. Bing Xue
6. Hangyu Zhu
7. Ye Tian
8. Linqiang Pan
9. Gary G. Yen
10. Chaoli Sun

Papers from Handing Wang's perspective

Ordered by how strongly the researchers Handing 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. 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. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Two_Arch2: An Improved Two-Archive Algorithm for Many-Objective Optimization (2015) — 500 citations
• Data-Driven Surrogate-Assisted Multiobjective Evolutionary Optimization of a Trauma System (2016) — 226 citations
• Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems (2017) — 356 citations
• Offline Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles (2019) — 213 citations
• A Random Forest-Assisted Evolutionary Algorithm for Data-Driven Constrained Multiobjective Combinatorial Optimization of Trauma Systems (2020) — 178 citations
• Data-Driven Evolutionary Optimization: An Overview and Case Studies (2019) — 582 citations
• A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization (2019) — 302 citations
• Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor (2020) — 266 citations