Yongchan Kwon

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 4 papers; trusts 22 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it
• Computer Vision and Pattern Recognition — 49 citations into it
• Optimization & Training — 15 citations into it
• Safety, Robustness & Interpretability — 6 citations into it

Whom Yongchan Kwon trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Kendall
4. Jimmy Ba
5. Kaiming He
6. Alex Krizhevsky
7. Yoshua Bengio
8. Alex Graves
9. Sergey Ioffe
10. Radford M. Neal

Who trusts Yongchan Kwon overall

1. Fan Yang
2. Alireza Mehrtash
3. Tharindu Fernando
4. Kumar Shridhar
5. Felix Laumann

Papers from Yongchan Kwon's perspective

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

Their papers

• Ensemble of Deep Convolutional Neural Networks for Prognosis of Ischemic Stroke (2016) — 46 citations
• Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning (2022) — 774 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