Qibin Zhao

Computational Mathematics · 2014–2017

A researcher in the Science corpus: 5 papers; trusts 63 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computational Mathematics — 150 citations into it; #301 of 612 by trust here, #130 by citations
• Computational Theory and Mathematics — 84 citations into it
• Artificial Intelligence — 75 citations into it
• Computational Mechanics — 63 citations into it; #5,902 of 10,001 by trust here, #7,173 by citations
• Signal Processing — 59 citations into it
• Computer Vision and Pattern Recognition — 45 citations into it
• Atomic and Molecular Physics, and Optics — 37 citations into it
• Numerical Methods & Scientific Computing — 18 citations into it

Whom Qibin Zhao trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Ivan V. Oseledets
2. Boris N. Khoromskij
3. Radford M. Neal
4. Tamara G. Kolda
5. Daniel Kressner
6. David John Cameron MacKay
7. Lars Grasedyck
8. Lieven De Lathauwer
9. Ruslan Salakhutdinov
10. Thorsten Rohwedder

Who trusts Qibin Zhao overall

1. Andrzej S. Cichocki
2. Renwei Dian
3. Evangelos E. Papalexakis
4. Johann A. Bengua
5. Menelaos Kanakis
6. Anton Obukhov
7. Christos Faloutsos
8. Leyuan Fang
9. Anh Huy Phan
10. Ho N. Phien

Papers from Qibin Zhao's perspective

Ordered by how strongly the researchers Qibin Zhao trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions (2016) — 600 citations
• Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives (2017) — 341 citations
• Tensor Ring Decomposition (2016) — 449 citations
• Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis (2014) — 1,400 citations
• Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination (2014) — 595 citations