Jihun Yi

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 96 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 57 citations into it; #9,290 of 35,345 by trust here, #20,377 by citations
• Computer Vision and Pattern Recognition — 23 citations into it
• Signal Processing — 23 citations into it
• Computer Networks and Communications — 16 citations into it; #1,816 of 9,886 by trust here, #4,594 by citations
• Representation & Self-Supervised Learning — 11 citations into it
• Control and Systems Engineering — 6 citations into it

Whom Jihun Yi trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Jimmy Ba
6. L. V. D. van der Maaten
7. Kaiming He
8. Yann LeCun
9. Lukas Ruff
10. Ilya Sutskever

Who trusts Jihun Yi overall

1. Hanqiu Deng
2. Denis A. Gudovskiy
3. Vitjan Zavrtanik
4. Guodong Wang
5. Chun-Liang Li
6. Yang Zou
7. Jiawei Yu1
8. Xingyu Li
9. Jongheon Jeong
10. Matej Kristan

Papers from Jihun Yi's perspective

Ordered by how strongly the researchers Jihun Yi trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation (2020) — 520 citations
• Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines (2021) — 521 citations