Bing Xue

Artificial Intelligence · Applied Soft Computing · 2013–2020

A researcher in the Science corpus: 16 papers; trusts 861 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 351 citations into it; #6,259 of 35,345 by trust here, #7,858 by citations
• Computer Vision and Pattern Recognition — 212 citations into it; #8,665 of 21,575 by trust here, #12,992 by citations
• Model Architectures — 115 citations into it
• Computational Theory and Mathematics — 101 citations into it; #3,789 of 12,182 by trust here, #3,117 by citations
• Neural Architecture Search & AutoML — 59 citations into it
• Optimization & Training — 52 citations into it
• Evaluation & Benchmarks — 34 citations into it
• Evolutionary Computation — 27 citations into it

Whom Bing Xue trusts overall

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

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

Who trusts Bing Xue overall

1. Yanan Sun
2. Mengjie Zhang
3. Shenkai Gu
4. Yu Xue
5. Yuqiao Liu
6. Yaochu Jin
7. Ran Cheng
8. Ye Tian
9. Emrah Hançer
10. Gehad Ismail Sayed

Papers from Bing Xue's perspective

Ordered by how strongly the researchers Bing Xue 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 Survey on Evolutionary Computation Approaches to Feature Selection (2016) — 1,551 citations
• Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach (2013) — 557 citations
• Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms (2014) — 566 citations
• A New Two-Stage Evolutionary Algorithm for Many-Objective Optimization (2019) — 110 citations
• Genetic programming for feature construction and selection in classification on high-dimensional data (2015) — 207 citations
• Differential evolution for filter feature selection based on information theory and feature ranking (2018) — 357 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 survey on swarm intelligence approaches to feature selection in data mining (2020) — 366 citations
• Self-Adaptive Particle Swarm Optimization for Large-Scale Feature Selection in Classification (2019) — 301 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