Sangwon Jung

Artificial Intelligence · 2020–2024

A researcher in the Science corpus: 3 papers; trusts 125 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 50 citations into it; #16,086 of 35,345 by trust here, #19,873 by citations
• Computer Vision and Pattern Recognition — 39 citations into it; #10,283 of 21,575 by trust here, #13,269 by citations
• Representation & Self-Supervised Learning — 26 citations into it
• Optimization & Training — 13 citations into it; #1,510 of 2,601 by trust here, #1,744 by citations
• Efficiency & Compression — 11 citations into it
• Meta & Continual Learning — 10 citations into it; #490 of 1,041 by trust here, #737 by citations
• Model Architectures — 7 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Sangwon Jung trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Kaiming He
5. Jaehong Yoon
6. Hui Zou
7. Yoshua Bengio
8. Ilya Sutskever
9. Yann LeCun
10. Jimmy Ba

Who trusts Sangwon Jung overall

1. Liyuan Wang
2. Jorge Armando Mendez Mendez
3. Rui Gao
4. Pengfei Yu
5. Xingxing Zhang
6. Sungmin Cha
7. Prakhar Kaushik
8. Hongjoon Ahn
9. Mustafa Burak Gurbuz
10. Qian Li

Papers from Sangwon Jung's perspective

Ordered by how strongly the researchers Sangwon Jung 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Continual Learning in the Presence of Spurious Correlations: Analyses and a Simple Baseline (2024) — 9 citations
• Continual Learning with Node-Importance based Adaptive Group Sparse Regularization (2020) — 163 citations
• Adaptive Group Sparse Regularization for Continual Learning (2020) — 8 citations