Jing Zhao
Artificial Intelligence · with Sun · 2005–2019
A researcher in the Science corpus: 3 papers; trusts 153 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 108 citations into it; #30,855 of 35,345 by trust here, #17,776 by citations
- Optimization & Training — 66 citations into it
- Computer Vision and Pattern Recognition — 62 citations into it
- Computational Mechanics — 30 citations into it
- Representation & Self-Supervised Learning — 24 citations into it
- Statistics and Probability — 22 citations into it
- Probabilistic & Bayesian Methods — 20 citations into it
- Computational Theory and Mathematics — 16 citations into it
Whom Jing Zhao trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Francis R. Bach
- Yoshua Bengio
- James Martens
- Radford M. Neal
- Michael I. Jordan
- Ilya Sutskever
- Diederik P. Kingma
- Alex Krizhevsky
- Yann LeCun
Who trusts Jing Zhao overall
Papers from Jing Zhao's perspective
Ordered by how strongly the researchers Jing Zhao 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)
Their papers
- Multi-view learning overview: Recent progress and new challenges (2017) — 935 citations
- A Survey of Optimization Methods From a Machine Learning Perspective (2019) — 672 citations
- Synthesis and microstructural characterization of Ti-Al3Ti metal-intermetallic laminate (MIL) composites (2005) — 186 citations