Junshui Ma
Artificial Intelligence · 2003–2018
A researcher in the Science corpus: 6 papers; trusts 6 researchers and is trusted by 33 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 22 citations into it; #7,259 of 35,345 by trust here, #18,586 by citations
- Computer Vision and Pattern Recognition — 12 citations into it; #11,310 of 21,575 by trust here, #16,367 by citations
- Optimization & Training — 7 citations into it
- Control and Systems Engineering — 5 citations into it; #3,542 of 8,257 by trust here, #4,722 by citations
Whom Junshui Ma trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Bernhard Schölkopf
- Chih-Jen Lin
- Alex Smola
- Andreas S. Weigend
- Gert Cauwenberghs
- Geoffrey E. Hinton
- Thorsten Joachims
- Tomaso A. Poggio
- Alex Krizhevsky
- Vladimir Naumovich Vapnik
Who trusts Junshui Ma overall
- Thierry Bouwmans
- Tolga Ergen
- Kyle Swanson
- Velibor V. Misic
- Garrett B. Goh
- Sebastian Schmidl
- Jie Yin
- Varun Chandola
- Qing Da
- Hyunjong Park
Papers from Junshui Ma's perspective
Ordered by how strongly the researchers Junshui Ma 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)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Going deeper with convolutions
Their papers
- Accurate On-line Support Vector Regression (2003) — 400 citations
- Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships (2015) — 1,045 citations
- Demystifying Multitask Deep Neural Networks for Quantitative Structure-Activity Relationships (2017) — 202 citations
- Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships (2016) — 468 citations
- Time-series novelty detection using one-class support vector machines (2003) — 411 citations
- Pembrolizumab in patients with advanced hepatocellular carcinoma previously treated with sorafenib (KEYNOTE-224): a non-randomised, open-label phase 2 trial. (2018) — 2,061 citations