Yanan Sun

Artificial Intelligence · with Yen · 2017–2020

A researcher in the Science corpus: 13 papers; trusts 896 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 319 citations into it; #7,775 of 35,345 by trust here, #9,557 by citations
• Computer Vision and Pattern Recognition — 196 citations into it; #8,322 of 21,575 by trust here, #11,721 by citations
• Model Architectures — 122 citations into it
• Computational Theory and Mathematics — 112 citations into it; #4,498 of 12,182 by trust here, #4,365 by citations
• Neural Architecture Search & AutoML — 61 citations into it
• Optimization & Training — 60 citations into it
• Management Science and Operations Research — 33 citations into it; #6,415 of 11,946 by trust here, #7,638 by citations
• Representation & Self-Supervised Learning — 30 citations into it

Whom Yanan Sun trusts overall

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

1. Alex Krizhevsky
2. Yoshua Bengio
3. Kalyanmoy Deb
4. Yann LeCun
5. Geoffrey E. Hinton
6. Kaiming He
7. L. Eon Bottou
8. Karen Simonyan
9. Ilya Sutskever
10. Christian Szegedy

Who trusts Yanan Sun overall

1. Bing Xue
2. Mengjie Zhang
3. Gary G. Yen
4. Yuqiao Liu
5. Handing Wang
6. Yaochu Jin
7. Ricardo H. R. Lima
8. Mohamed Reyad
9. Chen Wei
10. Hangyu Zhu

Papers from Yanan Sun's perspective

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

• A New Two-Stage Evolutionary Algorithm for Many-Objective Optimization (2019) — 110 citations
• Reference line-based Estimation of Distribution Algorithm for many-objective optimization (2017) — 25 citations
• Improved Regularity Model-Based EDA for Many-Objective Optimization (2018) — 43 citations
• IGD Indicator-Based Evolutionary Algorithm for Many-Objective Optimization Problems (2018) — 472 citations
• A Survey on Evolutionary Neural Architecture Search (2020) — 590 citations
• Completely Automated CNN Architecture Design Based on Blocks (2020) — 301 citations
• Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor (2020) — 266 citations
• A Hybrid Differential Evolution Approach to Designing Deep Convolutional Neural Networks for Image Classification (2018) — 63 citations
• Evolving Deep Convolutional Neural Networks by Variable-Length Particle Swarm Optimization for Image Classification (2018) — 184 citations
• Automatically Designing CNN Architectures Using Genetic Algorithm for Image Classification (2018) — 29 citations
• Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification (2018) — 729 citations
• Evolving Deep Convolutional Neural Networks for Image Classification (2017) — 659 citations
• Evolving Unsupervised Deep Neural Networks for Learning Meaningful Representations (2017) — 139 citations