Gyuhak Kim

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 7 papers; trusts 946 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 333 citations into it; #19,554 of 35,345 by trust here, #19,018 by citations
• Computer Vision and Pattern Recognition — 235 citations into it; #17,928 of 21,575 by trust here, #14,822 by citations
• Meta & Continual Learning — 206 citations into it
• Representation & Self-Supervised Learning — 91 citations into it
• Optimization & Training — 37 citations into it
• Radiology, Nuclear Medicine and Imaging — 33 citations into it
• Reinforcement Learning — 32 citations into it
• Natural Language Processing — 28 citations into it

Whom Gyuhak Kim trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Diederik P. Kingma
6. Arslan Chaudhry
7. Ilya Sutskever
8. Yann LeCun
9. Ross B. Girshick
10. Marc'Aurelio Ranzato

Who trusts Gyuhak Kim overall

1. Changnan Xiao
2. Bin Liu
3. Zixuan Ke
4. Tatsuya Konishi
5. Bing Liu
6. Da-Wei Zhou
7. Sahisnu Mazumder
8. Qiwen Wang

Papers from Gyuhak Kim's perspective

Ordered by how strongly the researchers Gyuhak Kim 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
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

• Open-World Continual Learning: Unifying Novelty Detection and Continual Learning (2023) — 34 citations
• Learnability and Algorithm for Continual Learning (2023) — 37 citations
• Continual Pre-training of Language Models (2023) — 242 citations
• Continual Learning Based on OOD Detection and Task Masking (2022) — 29 citations
• A Theoretical Study on Solving Continual Learning (2022) — 111 citations
• A Multi-Head Model for Continual Learning via Out-of-Distribution Replay (2022) — 47 citations
• Continual Learning via Principal Components Projection (2019) — 3 citations