Jongyeong Lee

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 16 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 12 citations into it; #35,132 of 35,345 by trust here, #29,412 by citations
• Evaluation & Benchmarks — 5 citations into it

Whom Jongyeong Lee trusts overall

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

1. Alex Krizhevsky
2. Tong Zhang
3. Chih-Jen Lin
4. Yann LeCun
5. Peter L. Bartlett
6. Geoffrey E. Hinton
7. Seiya Tokui
8. Nitish Srivastava
9. Sashank J. Reddi
10. Gilles Blanchard

Who trusts Jongyeong Lee overall

1. Michal Lukasik

Papers from Jongyeong Lee's perspective

Ordered by how strongly the researchers Jongyeong 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. Learning Multiple Layers of Features from Tiny Images
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Dropout: a simple way to prevent neural networks from overfitting
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Long Short-Term Memory
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• On Symmetric Losses for Learning from Corrupted Labels (2019) — 116 citations