Qingjiang Shi
Electrical and Electronic Engineering · 2011–2017
A researcher in the Science corpus: 4 papers; trusts 24 researchers and is trusted by 43 researchers here, as inferred from the citation graph.
Subfields
- Electrical and Electronic Engineering — 38 citations into it; #613 of 16,539 by trust here, #3,696 by citations
- Artificial Intelligence — 23 citations into it; #27,882 of 35,345 by trust here, #20,377 by citations
- Computer Networks and Communications — 23 citations into it; #1,251 of 9,886 by trust here, #1,864 by citations
- Aerospace Engineering — 9 citations into it
- Model Architectures — 9 citations into it
- Numerical Analysis — 5 citations into it
- Optimization & Training — 5 citations into it
- Signal Processing — 5 citations into it; #4,890 of 7,572 by trust here, #2,911 by citations
Whom Qingjiang Shi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Rui Zhang
- Zhi-Quan Tom Luo
- Liang Liu
- Seung-Jun Kim
- Erik G. Larsson
- Xun Zhou
- Sigen Ye
- Chin Keong Ho
- Jianwei Huang
- Krishna Srikanth Gomadam
Who trusts Qingjiang Shi overall
- Woongsup Lee
- Minhoe Kim
- Meisam Razaviyayn
- Hoon Lee
- Wei Yu
- Mingyi Hong
- Kaiming Shen
- Foad Sohrabi
- Haoran Sun
- Yasar Sinan Nasir
Papers from Qingjiang Shi's perspective
Ordered by how strongly the researchers Qingjiang Shi trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Rectified Linear Units Improve Restricted Boltzmann Machines
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- An Iteratively Weighted MMSE Approach to Distributed Sum-Utility Maximization for a MIMO Interfering Broadcast Channel (2011) — 2,193 citations
- Joint Transmit Beamforming and Receive Power Splitting for MISO SWIPT Systems (2013) — 515 citations
- Learning to Optimize: Training Deep Neural Networks for Interference Management (2017) — 725 citations
- Learning to optimize: Training deep neural networks for wireless resource management (2017) — 357 citations