Tae-Seong Kim

Computer Vision and Pattern Recognition · 2010–2018

A researcher in the Science corpus: 3 papers; trusts 40 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 48 citations into it
• Artificial Intelligence — 33 citations into it
• Model Architectures — 5 citations into it

Whom Tae-Seong Kim trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Karen Simonyan
4. Ilya Sutskever

Papers from Tae-Seong Kim's perspective

Ordered by how strongly the researchers Tae-Seong Kim trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Dropout: a simple way to prevent neural networks from overfitting
5. Gradient-based learning applied to document recognition
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Going deeper with convolutions
10. Long Short-Term Memory

Their papers

• A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer (2010) — 566 citations
• A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification (2018) — 354 citations
• Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks (2018) — 428 citations