Yoojin Choi

Computer Vision and Pattern Recognition · 2016–2021

A researcher in the Science corpus: 4 papers; trusts 372 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 72 citations into it; #8,121 of 21,575 by trust here, #11,950 by citations
• Artificial Intelligence — 57 citations into it; #15,693 of 35,345 by trust here, #20,836 by citations
• Efficiency & Compression — 39 citations into it; #565 of 1,264 by trust here, #1,016 by citations
• Optimization & Training — 18 citations into it
• Generative Models — 13 citations into it
• Model Architectures — 12 citations into it
• Representation & Self-Supervised Learning — 12 citations into it
• Signal Processing — 11 citations into it

Whom Yoojin Choi trusts overall

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

1. Diederik P. Kingma
2. Alex Krizhevsky
3. Kaiming He
4. Geoffrey E. Hinton
5. Yann LeCun
6. Yoshua Bengio
7. Jimmy Ba
8. Ilya Sutskever
9. X. Zhang
10. Karen Simonyan

Who trusts Yoojin Choi overall

1. Yunhui Guo
2. Yu Cheng
3. Zhihao Duan
4. Eirikur Agustsson
5. Simon Wiedemann
6. Alexandre D'efossez
7. Tailin Liang
8. Duo Wang
9. Minsik Cho
10. Davis W. Blalock

Papers from Yoojin Choi's perspective

Ordered by how strongly the researchers Yoojin 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
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. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Towards the Limit of Network Quantization (2016) — 217 citations
• Dual-Teacher Class-Incremental Learning With Data-Free Generative Replay (2021) — 50 citations
• Variable Rate Deep Image Compression With a Conditional Autoencoder (2019) — 280 citations
• Jointly Sparse Convolutional Neural Networks in Dual Spatial-winograd Domains (2019) — 6 citations