Donggyu Joo

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 330 researchers and is trusted by 49 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it; #5,159 of 35,345 by trust here, #3,998 by citations
• Computer Vision and Pattern Recognition — 62 citations into it; #4,265 of 21,575 by trust here, #3,141 by citations
• Optimization & Training — 32 citations into it
• Model Architectures — 22 citations into it
• Representation & Self-Supervised Learning — 17 citations into it
• Efficiency & Compression — 6 citations into it; #192 of 1,264 by trust here, #207 by citations
• Evaluation & Benchmarks — 6 citations into it
• Statistical and Nonlinear Physics — 6 citations into it

Whom Donggyu Joo trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Sergey Ioffe
6. Yann LeCun
7. Christian Szegedy
8. X. Zhang
9. Karen Simonyan
10. Ilya Sutskever

Who trusts Donggyu Joo overall

1. Nikolaos Passalis
2. Zhiqiang Shen
3. Byeongho Heo
4. Seunghyun Lee
5. Linfeng Zhang
6. Chenglin Yang
7. Mingi Ji
8. Seyed Iman Mirzadeh
9. Hanting Chen
10. Jangho Kim

Papers from Donggyu Joo's perspective

Ordered by how strongly the researchers Donggyu Joo 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. 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

• A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning (2017) — 1,725 citations
• Continual Learning With Extended Kronecker-Factored Approximate Curvature (2020) — 72 citations
• Residual Continual Learning (2020) — 25 citations