Juho Lee

Artificial Intelligence · with Kim · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 300 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 78 citations into it
• Computer Vision and Pattern Recognition — 64 citations into it
• Representation & Self-Supervised Learning — 47 citations into it
• Model Architectures — 22 citations into it
• Optimization & Training — 14 citations into it
• Meta & Continual Learning — 11 citations into it
• Probabilistic & Bayesian Methods — 11 citations into it
• Media Technology — 8 citations into it

Whom Juho Lee trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Graves
4. Alex Krizhevsky
5. Oriol Vinyals
6. Yoshua Bengio
7. Jimmy Ba
8. Ruslan Salakhutdinov
9. Radford M. Neal
10. Ilya Sutskever

Who trusts Juho Lee overall

1. Yao-Hung Hubert Tsai
2. Timothy M. Hospedales
3. Adam R. Kosiorek
4. Xiaohua Zhai
5. David W. Zhang
6. Wolfgang Stammer
7. Nico Engel
8. Kuan Fang
9. Chanyoung Park
10. Cynthia Rudin

Papers from Juho Lee's perspective

Ordered by how strongly the researchers Juho Lee 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. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Set Transformer (2018) — 267 citations
• Learning to Propagate Labels: Transductive Propagation Network for Few-Shot Learning (2018) — 758 citations
• Deep Amortized Clustering (2019) — 24 citations
• Transductive Propagation Network for Few-shot Learning (2018) — 81 citations