Joong-Ho Won

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 3 papers; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 24 citations into it
• Optimization & Training — 9 citations into it

Whom Joong-Ho Won trusts overall

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

1. Diederik P. Kingma
2. Alex Kendall
3. Jimmy Ba
4. Yarin Gal

Papers from Joong-Ho Won's perspective

Ordered by how strongly the researchers Joong-Ho Won 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. Going deeper with convolutions
9. Long Short-Term Memory
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Ensemble of Deep Convolutional Neural Networks for Prognosis of Ischemic Stroke (2016) — 46 citations
• Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation (2020) — 419 citations
• Uncertainty quantification using Bayesian neural networks in classification: Application to ischemic stroke lesion segmentation (2018) — 108 citations