Joonseok Lee

Computer Vision and Pattern Recognition · 2013–2020

A researcher in the Science corpus: 5 papers; trusts 91 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 49 citations into it; #16,082 of 21,575 by trust here, #4,067 by citations
• Artificial Intelligence — 43 citations into it; #26,415 of 35,345 by trust here, #5,638 by citations
• Representation & Self-Supervised Learning — 17 citations into it
• Information Systems — 14 citations into it
• Management Science and Operations Research — 14 citations into it
• Graph Learning — 11 citations into it
• Optimization & Training — 8 citations into it
• Recommender Systems — 8 citations into it

Whom Joonseok Lee trusts overall

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

1. E.J. Candes
2. Yehuda Koren
3. Ruslan Salakhutdinov
4. Geoffrey E. Hinton
5. Dennis DeCoste
6. Alex Krizhevsky
7. Sam T. Roweis
8. Diederik P. Kingma
9. Yoshua Bengio
10. Ilya Sutskever

Who trusts Joonseok Lee overall

1. Gintare Karolina Dziugaite
2. Florian Strub
3. Guang-He Lee
4. Yin Zheng
5. Da Kuang
6. Suvash Sedhain
7. Jianxun Lian
8. Luisa Verdoliva
9. Daniel M. Roy
10. Jérémie Mary

Papers from Joonseok Lee's perspective

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

Their papers

• YouTube-8M: A Large-Scale Video Classification Benchmark (2016) — 1,406 citations
• Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing (2020) — 334 citations
• Learning multiple-question decision trees for cold-start recommendation (2013) — 94 citations
• N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification (2018) — 295 citations
• Local Low-Rank Matrix Approximation (2013) — 216 citations