Kookjin Lee
Statistical and Nonlinear Physics · 2018
A researcher in the Science corpus: 1 paper; trusts 43 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Statistical and Nonlinear Physics — 17 citations into it; #2,658 of 10,127 by trust here, #6,156 by citations
- Computational Mechanics — 13 citations into it
- Computer Vision and Pattern Recognition — 9 citations into it
- Representation & Self-Supervised Learning — 9 citations into it
- Optimization & Training — 8 citations into it
- Artificial Intelligence — 7 citations into it
- Statistics and Probability — 5 citations into it; #4,338 of 12,932 by trust here, #7,165 by citations
Whom Kookjin Lee trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Teuvo Kohonen
- Diederik P. Kingma
- Yoshua Bengio
- Neil D. Lawrence
- Kevin T. Carlberg
- Kaiming He
- L. Eon Bottou
- Yann LeCun
- Xavier Glorot
Who trusts Kookjin Lee overall
- Stefania Fresca
- Luning Sun
- Romit Maulik
- Andrea Manzoni
- Jian-Xun Wang
- Bethany Lusch
- Luca Dede'
- Ricardo Vinuesa
- Steven L. Brunton
- Prasanna Balaprakash
Papers from Kookjin Lee's perspective
Ordered by how strongly the researchers Kookjin Lee trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)