Young Geun Kim

Computer Vision and Pattern Recognition · 2020–2021

A researcher in the Science corpus: 4 papers; trusts 409 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 76 citations into it
• Electrical and Electronic Engineering — 75 citations into it; #7,135 of 16,539 by trust here, #10,154 by citations
• Computer Networks and Communications — 67 citations into it; #5,111 of 9,886 by trust here, #7,492 by citations
• Artificial Intelligence — 64 citations into it
• ML Systems & Hardware — 59 citations into it
• Efficiency & Compression — 50 citations into it; #705 of 1,264 by trust here, #1,174 by citations
• Hardware and Architecture — 42 citations into it
• Optimization & Training — 19 citations into it

Whom Young Geun Kim trusts overall

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

1. Carole-Jean Wu
2. Kaiming He
3. Quoc V. Le
4. Mingxing Tan
5. Alex Krizhevsky
6. Geoffrey E. Hinton
7. Barret Zoph
8. Tian Guo
9. Jia Deng
10. Yann LeCun

Who trusts Young Geun Kim overall

1. Carole-Jean Wu
2. Roberto Verdecchia
3. Udit Gupta
4. Jesse Dodge
5. June Sallou
6. Luís Cruz

Papers from Young Geun Kim's perspective

Ordered by how strongly the researchers Young Geun Kim 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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

• AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning (2021) — 117 citations
• Chasing Carbon: The Elusive Environmental Footprint of Computing (2020) — 376 citations
• AutoScale: Energy Efficiency Optimization for Stochastic Edge Inference Using Reinforcement Learning (2020) — 78 citations
• AutoScale: Optimizing Energy Efficiency of End-to-End Edge Inference under Stochastic Variance (2020) — 4 citations