Sungwoong Kim

Computer Vision and Pattern Recognition · 2014–2020

A researcher in the Science corpus: 6 papers; trusts 244 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 109 citations into it; #15,792 of 21,575 by trust here, #6,606 by citations
• Artificial Intelligence — 107 citations into it; #16,247 of 35,345 by trust here, #5,834 by citations
• Optimization & Training — 35 citations into it
• Representation & Self-Supervised Learning — 29 citations into it
• Model Architectures — 23 citations into it
• Graph Learning — 17 citations into it
• Probabilistic & Bayesian Methods — 13 citations into it; #1,483 of 1,525 by trust here, #477 by citations
• Meta & Continual Learning — 10 citations into it; #1,008 of 1,041 by trust here, #384 by citations

Whom Sungwoong Kim trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yoshua Bengio
5. Diederik P. Kingma
6. Yann LeCun
7. Karen Simonyan
8. Ilya Sutskever
9. Radford M. Neal
10. Oriol Vinyals

Who trusts Sungwoong Kim overall

1. Woong Bae
2. Zhihua Liu

Papers from Sungwoong Kim's perspective

Ordered by how strongly the researchers Sungwoong Kim 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Fast AutoAugment (2019) — 659 citations
• Bayesian Model-Agnostic Meta-Learning (2018) — 548 citations
• A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems (2014) — 190 citations
• Scalable Neural Architecture Search for 3D Medical Image Segmentation (2019) — 84 citations
• Edge-Labeling Graph Neural Network for Few-Shot Learning (2019) — 529 citations
• torchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models (2020) — 63 citations