Chao-Kai Wen

Electrical and Electronic Engineering · 2015–2020

A researcher in the Science corpus: 11 papers; trusts 55 researchers and is trusted by 45 researchers here, as inferred from the citation graph.

Subfields

• Electrical and Electronic Engineering — 65 citations into it; #595 of 16,539 by trust here, #3,260 by citations
• Computer Networks and Communications — 37 citations into it; #8,014 of 9,886 by trust here, #8,191 by citations
• Aerospace Engineering — 35 citations into it; #629 of 6,421 by trust here, #945 by citations
• Artificial Intelligence — 33 citations into it; #11,150 of 35,345 by trust here, #11,192 by citations
• Computational Mechanics — 31 citations into it
• Signal Processing — 31 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it
• Biomedical Engineering — 8 citations into it

Whom Chao-Kai Wen trusts overall

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

1. Christopher A. Metzler
2. David L. Donoho
3. Kaiming He
4. Arian Maleki
5. Ingrid Daubechies
6. T. Marzetta
7. Xiongbin Rao
8. Ping-Heng Kuo
9. Hao Ye
10. Tianqi Wang

Who trusts Chao-Kai Wen overall

1. Geoffrey Y. Li
2. Ahmed Alkhateeb
3. Osvaldo Simeone
4. Shi Jin
5. Peihao Dong
6. Zhijin Qin
7. Jianhua Mo
8. Hengtao He
9. Tianqi Wang
10. Jiajia Guo

Papers from Chao-Kai Wen's perspective

Ordered by how strongly the researchers Chao-Kai Wen trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
5. Gradient-based learning applied to document recognition
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. ImageNet: A large-scale hierarchical image database
8. Long Short-Term Memory
9. Attention is All you Need (Neural Information Processing Systems)
10. Going deeper with convolutions

Their papers

• Large Intelligent Surface-Assisted Wireless Communication Exploiting Statistical CSI (2018) — 798 citations
• Deep Learning for Massive MIMO CSI Feedback (2017) — 1,134 citations
• Deep Reinforcement Learning Based Intelligent Reflecting Surface Optimization for MISO Communication Systems (2020) — 324 citations
• Deep learning for wireless physical layer: Opportunities and challenges (2017) — 572 citations
• Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems (2018) — 750 citations
• Uplink Achievable Rate for Massive MIMO Systems With Low-Resolution ADC (2015) — 342 citations
• Model-Driven Deep Learning for Physical Layer Communications (2018) — 438 citations
• Bayes-Optimal Joint Channel-and-Data Estimation for Massive MIMO With Low-Precision ADCs (2015) — 342 citations
• Deep Learning-Based CSI Feedback Approach for Time-Varying Massive MIMO Channels (2018) — 411 citations
• Model-Driven Deep Learning for MIMO Detection (2020) — 419 citations
• Convolutional Neural Network-Based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis (2019) — 297 citations