Shiliang Sun
Artificial Intelligence · 2006–2019
A researcher in the Science corpus: 9 papers; trusts 428 researchers and is trusted by 49 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 255 citations into it; #5,497 of 35,345 by trust here, #8,157 by citations
- Computer Vision and Pattern Recognition — 121 citations into it; #5,564 of 21,575 by trust here, #8,409 by citations
- Optimization & Training — 68 citations into it
- Representation & Self-Supervised Learning — 63 citations into it; #943 of 3,145 by trust here, #1,582 by citations
- Probabilistic & Bayesian Methods — 44 citations into it; #1,308 of 1,525 by trust here, #1,515 by citations
- Natural Language Processing — 40 citations into it
- Statistics and Probability — 36 citations into it; #10,312 of 12,932 by trust here, #12,603 by citations
- Computational Mechanics — 34 citations into it
Whom Shiliang Sun trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- James Martens
- Yoshua Bengio
- Ilya Sutskever
- Radford M. Neal
- Diederik P. Kingma
- Alex Krizhevsky
- Michael I. Jordan
- Yann LeCun
- Richard Socher
Who trusts Shiliang Sun overall
- Shahaboddin Shamshirband
- Mingxia Liu
- Kasra Mohammadi
- Yongfeng Zhang
- Garrett Wilson
- Nicholas G. Polson
- Li Yang
- Chong Wen Tong
- Yuxing Sun
- Taoying Li
Papers from Shiliang Sun's perspective
Ordered by how strongly the researchers Shiliang Sun 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- A survey of multi-view machine learning (2013) — 862 citations
- Multi-view learning overview: Recent progress and new challenges (2017) — 935 citations
- PAC-bayes bounds with data dependent priors (2012) — 140 citations
- Dynamical Sampling with Langevin Normalization Flows (2019) — 5 citations
- A Survey of Optimization Methods From a Machine Learning Perspective (2019) — 672 citations
- A Survey on Multi-View Clustering (2017) — 344 citations
- A review of natural language processing techniques for opinion mining systems (2017) — 518 citations
- A survey of multi-source domain adaptation (2015) — 334 citations
- A bayesian network approach to traffic flow forecasting (2006) — 723 citations