Jinwon Lee

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 3 papers; trusts 150 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 61 citations into it; #8,563 of 35,345 by trust here, #13,962 by citations
• Computer Vision and Pattern Recognition — 59 citations into it; #15,427 of 21,575 by trust here, #13,542 by citations
• Efficiency & Compression — 39 citations into it
• Model Architectures — 16 citations into it
• Optimization & Training — 14 citations into it
• Signal Processing — 9 citations into it; #1,626 of 7,572 by trust here, #3,784 by citations
• Speech & Audio — 5 citations into it

Whom Jinwon Lee trusts overall

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

1. Yoshua Bengio
2. Kaiming He
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Quoc V. Le
7. Sergey Ioffe
8. Yann LeCun
9. Jimmy Ba
10. Christian Szegedy

Who trusts Jinwon Lee overall

1. Mira Lilleholt Vik
2. Ke Tan
3. Markus Nagel
4. Cassia Valentini-Botinhao
5. Daniel Michelsanti
6. Deliang Wang
7. Ziyue Zhao
8. Yelysei Bondarenko
9. Kilian Schulze-Forster
10. Umut Isik

Papers from Jinwon Lee's perspective

Ordered by how strongly the researchers Jinwon Lee 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• LSQ+: Improving low-bit quantization through learnable offsets and better initialization (2020) — 310 citations
• QKD: Quantization-aware Knowledge Distillation (2019) — 82 citations
• A Fully Convolutional Neural Network for Speech Enhancement (2016) — 401 citations