Myunghee Cho Paik

Artificial Intelligence · 2004–2020

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

Subfields

• Artificial Intelligence — 25 citations into it
• Optimization & Training — 9 citations into it

Whom Myunghee Cho Paik trusts overall

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

1. Diederik P. Kingma

Who trusts Myunghee Cho Paik overall

1. Sarah Nogueira
2. Konstantinos Sechidis
3. Gavin Brown

Papers from Myunghee Cho Paik's perspective

Ordered by how strongly the researchers Myunghee Cho Paik 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. What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
5. Attention is All you Need (Neural Information Processing Systems)
6. Learning Multiple Layers of Features from Tiny Images
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• The measurement of interrater agreement (2004) — 1,271 citations
• 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