Yoonho Lee

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 5 papers; trusts 382 researchers and is trusted by 49 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 114 citations into it; #27,779 of 35,345 by trust here, #9,760 by citations
• Computer Vision and Pattern Recognition — 68 citations into it; #17,002 of 21,575 by trust here, #8,831 by citations
• Representation & Self-Supervised Learning — 43 citations into it; #658 of 3,145 by trust here, #1,540 by citations
• Model Architectures — 18 citations into it
• Optimization & Training — 18 citations into it
• Reinforcement Learning — 16 citations into it
• Meta & Continual Learning — 14 citations into it
• Probabilistic & Bayesian Methods — 11 citations into it

Whom Yoonho Lee trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Oriol Vinyals
5. Sergey Ioffe
6. J. Urgen Schmidhuber
7. Jimmy Ba
8. Yoshua Bengio
9. Chelsea Finn
10. Ruslan Salakhutdinov

Who trusts Yoonho Lee overall

1. Han-Jia Ye
2. Johannes von Oswald
3. Timothy M. Hospedales
4. Yaoyao Liu
5. Hexiang Hu
6. Sebastian Flennerhag
7. Andrei A. Rusu
8. Huaxiu Yao
9. Shixiang Tang
10. Sébastien M. R. Arnold

Papers from Yoonho Lee's perspective

Ordered by how strongly the researchers Yoonho Lee 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Set Transformer (2018) — 267 citations
• Meta-Learning with Adaptive Layerwise Metric and Subspace (2018) — 27 citations
• Deep Amortized Clustering (2019) — 24 citations
• DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature (2023) — 1,117 citations
• Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace (2018) — 375 citations