Gyeong-Hun Park

Computer Vision and Pattern Recognition · 2018

A researcher in the Science corpus: 1 paper; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 10 citations into it

Whom Gyeong-Hun Park trusts overall

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

1. Alex Krizhevsky

Papers from Gyeong-Hun Park's perspective

Ordered by how strongly the researchers Gyeong-Hun Park trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Gradient-based learning applied to document recognition
3. ImageNet Large Scale Visual Recognition Challenge
4. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
5. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Dropout: a simple way to prevent neural networks from overfitting
7. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
8. Long Short-Term Memory
9. Attention is All you Need (Neural Information Processing Systems)
10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers

• Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm. (2018) — 618 citations