Luo Luo
Artificial Intelligence · with Zhang · 2016–2020
A researcher in the Science corpus: 3 papers; trusts 35 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 38 citations into it; #27,884 of 35,345 by trust here, #30,347 by citations
- Optimization & Training — 37 citations into it; #1,817 of 2,601 by trust here, #2,313 by citations
- Computational Mechanics — 29 citations into it; #7,878 of 10,001 by trust here, #8,258 by citations
- Computational Theory and Mathematics — 8 citations into it
- Statistics and Probability — 7 citations into it; #10,361 of 12,932 by trust here, #10,855 by citations
- Management Science and Operations Research — 6 citations into it
- Numerical Analysis — 5 citations into it
Whom Luo Luo trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Zeyuan Allen-Zhu
- Aaron Defazio
- Yurii E. Nesterov
- Francis R. Bach
- Yuchen Zhang
- Mark W. Schmidt
- Ian J. Goodfellow
- Elad Hazan
- Geoffrey E. Hinton
- Balamurugan Palaniappan
Who trusts Luo Luo overall
Papers from Luo Luo's perspective
Ordered by how strongly the researchers Luo Luo trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Learning Multiple Layers of Features from Tiny Images
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- 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)
Their papers
- Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems (2020) — 145 citations
- Approximate Newton Methods and Their Local Convergence (2017) — 26 citations
- Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features (2016) — 3 citations