Xiaowen Chu

Artificial Intelligence · 2013–2020

A researcher in the Science corpus: 7 papers; trusts 733 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 264 citations into it; #15,745 of 35,345 by trust here, #7,241 by citations
• Computer Vision and Pattern Recognition — 236 citations into it; #18,997 of 21,575 by trust here, #7,092 by citations
• Neural Architecture Search & AutoML — 114 citations into it; #136 of 283 by trust here, #139 by citations
• Optimization & Training — 78 citations into it
• Model Architectures — 55 citations into it
• Efficiency & Compression — 45 citations into it
• Evaluation & Benchmarks — 39 citations into it
• Computational Theory and Mathematics — 20 citations into it

Whom Xiaowen Chu trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yoshua Bengio
5. Yann LeCun
6. Ilya Sutskever
7. Diederik P. Kingma
8. Karen Simonyan
9. Alex Graves
10. Ross B. Girshick

Who trusts Xiaowen Chu overall

1. Marc-André Zöller

Papers from Xiaowen Chu's perspective

Ordered by how strongly the researchers Xiaowen Chu 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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
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

• Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes (2018) — 423 citations
• AutoML: A Survey of the State-of-the-Art (2019) — 1,830 citations
• IRS: A Large Synthetic Indoor Robotics Stereo Dataset for Disparity and Surface Normal Estimation (2019) — 25 citations
• Communication-Efficient Decentralized Learning with Sparsification and Adaptive Peer Selection (2020) — 72 citations
• Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans (2020) — 61 citations
• Benchmarking State-of-the-Art Deep Learning Software Tools (2016) — 343 citations
• Electric Vehicle Charging Station Placement: Formulation, Complexity, and Solutions (2013) — 438 citations