Woongsup Lee
Electrical and Electronic Engineering · 2018–2019
A researcher in the Science corpus: 4 papers; trusts 21 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Electrical and Electronic Engineering — 23 citations into it; #2,272 of 16,539 by trust here, #10,154 by citations
- Artificial Intelligence — 16 citations into it; #14,005 of 35,345 by trust here, #34,213 by citations
- Computer Networks and Communications — 12 citations into it; #2,656 of 9,886 by trust here, #8,844 by citations
- Computer Vision and Pattern Recognition — 10 citations into it
- Signal Processing — 10 citations into it
Whom Woongsup Lee trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Qingjiang Shi
- Hao Ye
- Jimmy Ba
- Karen Simonyan
- Alex Krizhevsky
- Tim O'Shea
- Haoran Sun
- Geoffrey Y. Li
- Geoffrey E. Hinton
Who trusts Woongsup Lee overall
- Hoon Lee
- Sang Hyun Lee
- Tony Q. S. Quek
- Yaohua Sun
- Deniz Gündüz
- Wenchao Xia
- Changyang She
- Paul de Kerret
- David Gesbert
- Harald Bayerlein
Papers from Woongsup Lee's perspective
Ordered by how strongly the researchers Woongsup Lee trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Deep Power Control: Transmit Power Control Scheme Based on Convolutional Neural Network (2018) — 268 citations
- Deep Learning Based Transmit Power Control in Underlaid Device-to-Device Communication (2019) — 44 citations
- Transmit Power Control Using Deep Neural Network for Underlay Device-to-Device Communication (2018) — 65 citations
- Resource Allocation for Multi-Channel Underlay Cognitive Radio Network Based on Deep Neural Network (2018) — 99 citations