Eunhyeok Park

Computer Vision and Pattern Recognition · 2015–2022

A researcher in the Science corpus: 4 papers; trusts 325 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 82 citations into it; #9,100 of 21,575 by trust here, #4,372 by citations
• Artificial Intelligence — 61 citations into it; #15,309 of 35,345 by trust here, #15,542 by citations
• Efficiency & Compression — 33 citations into it; #510 of 1,264 by trust here, #318 by citations
• Model Architectures — 18 citations into it
• Optimization & Training — 16 citations into it
• Signal Processing — 11 citations into it
• Hardware and Architecture — 10 citations into it
• Computational Mechanics — 7 citations into it

Whom Eunhyeok Park trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Yoshua Bengio
6. Christian Szegedy
7. Shaoqing Ren
8. Yann LeCun
9. X. Zhang
10. Ross B. Girshick

Who trusts Eunhyeok Park overall

1. Yunhui Guo
2. Jungwook Choi
3. Frederick Tung
4. Duseok Kang
5. Md Maruf Hossain Shuvo
6. Assi Barak
7. Julian Faraone
8. Chenglong Zhao
9. Tianyun Zhang
10. Shaohui Lin

Papers from Eunhyeok Park's perspective

Ordered by how strongly the researchers Eunhyeok Park 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. Long Short-Term Memory
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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications (2015) — 949 citations
• Weighted-Entropy-Based Quantization for Deep Neural Networks (2017) — 272 citations
• Big/little deep neural network for ultra low power inference (2015) — 151 citations
• NIPQ: Noise Injection Pseudo Quantization for Automated DNN Optimization (2022) — 12 citations