Jae-Gil Lee

Artificial Intelligence · with Song · 2019–2023

A researcher in the Science corpus: 5 papers; trusts 287 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 223 citations into it
• Computer Vision and Pattern Recognition — 102 citations into it
• Optimization & Training — 60 citations into it
• Safety, Robustness & Interpretability — 60 citations into it
• Representation & Self-Supervised Learning — 31 citations into it
• Evaluation & Benchmarks — 23 citations into it
• Safety Research — 22 citations into it
• Management Science and Operations Research — 14 citations into it

Whom Jae-Gil Lee trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Christian Szegedy
4. Ilya Sutskever
5. Yoshua Bengio
6. Kaiming He
7. Sergey Ioffe
8. Diederik P. Kingma
9. Yann LeCun
10. Radford M. Neal

Who trusts Jae-Gil Lee overall

1. Hwanjun Song
2. Steven Euijong Whang

Papers from Jae-Gil Lee's perspective

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

Their papers

• Learning From Noisy Labels With Deep Neural Networks: A Survey (2020) — 1,409 citations
• One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning (2023) — 7 citations
• Data collection and quality challenges in deep learning: a data-centric AI perspective (2021) — 559 citations
• Robust Learning by Self-Transition for Handling Noisy Labels (2020) — 46 citations
• SELFIE: Refurbishing Unclean Samples for Robust Deep Learning (2019) — 467 citations