Jungwook Choi

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 6 papers; trusts 245 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 104 citations into it; #16,503 of 35,345 by trust here, #6,796 by citations
• Computer Vision and Pattern Recognition — 94 citations into it; #10,478 of 21,575 by trust here, #4,926 by citations
• Efficiency & Compression — 56 citations into it
• Model Architectures — 32 citations into it
• Optimization & Training — 29 citations into it
• Signal Processing — 12 citations into it
• Statistical and Nonlinear Physics — 7 citations into it; #4,907 of 10,127 by trust here, #976 by citations
• Media Technology — 6 citations into it

Whom Jungwook Choi trusts overall

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

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

Who trusts Jungwook Choi overall

1. Zhen Dong
2. Xiao Sun
3. Yuhang Li
4. Ji Lin
5. Zi-Mei Yao
6. Qing Jin
7. Kuan Wang
8. Tim Dettmers
9. Ruihao Gong
10. Markus Nagel

Papers from Jungwook Choi's perspective

Ordered by how strongly the researchers Jungwook Choi 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• PACT: Parameterized Clipping Activation for Quantized Neural Networks (2018) — 1,126 citations
• Training Deep Neural Networks with 8-bit Floating Point Numbers (2018) — 566 citations
• AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training (2017) — 188 citations
• Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks (2020) — 37 citations
• Accurate and Efficient 2-bit Quantized Neural Networks (2019) — 172 citations
• Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks (2019) — 255 citations