Sungmin Cha

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 7 papers; trusts 597 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 182 citations into it; #6,267 of 35,345 by trust here, #7,657 by citations
• Computer Vision and Pattern Recognition — 127 citations into it; #4,882 of 21,575 by trust here, #5,507 by citations
• Representation & Self-Supervised Learning — 58 citations into it
• Meta & Continual Learning — 48 citations into it; #144 of 1,041 by trust here, #184 by citations
• Optimization & Training — 38 citations into it; #756 of 2,601 by trust here, #912 by citations
• Model Architectures — 22 citations into it
• Efficiency & Compression — 21 citations into it
• Evaluation & Benchmarks — 20 citations into it

Whom Sungmin Cha trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Yann LeCun
7. Christian Szegedy
8. Ilya Sutskever
9. Jia Deng
10. Ross B. Girshick

Who trusts Sungmin Cha overall

1. Liyuan Wang
2. Jorge Armando Mendez Mendez
3. Rui Gao
4. Xingxing Zhang
5. Eric Eaton
6. Weiwei Liu
7. Huyong Wang
8. Beomyoung Kim
9. Youngmin Oh
10. Zekang Zhang

Papers from Sungmin Cha's perspective

Ordered by how strongly the researchers Sungmin Cha 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
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

• Uncertainty-based Continual Learning with Adaptive Regularization (2019) — 274 citations
• Task-Balanced Batch Normalization for Exemplar-based Class-Incremental Learning (2022) — 3 citations
• Knowledge Unlearning for Mitigating Privacy Risks in Language Models (2022) — 490 citations
• SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning (2021) — 135 citations
• CPR: Classifier-Projection Regularization for Continual Learning (2020) — 91 citations
• Continual Learning with Node-Importance based Adaptive Group Sparse Regularization (2020) — 163 citations
• Adaptive Group Sparse Regularization for Continual Learning (2020) — 8 citations