Anh Huy Phan

Computational Mathematics · 2009–2017

A researcher in the Science corpus: 8 papers; trusts 62 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Computational Mathematics — 172 citations into it; #57 of 612 by trust here, #29 by citations
• Artificial Intelligence — 95 citations into it; #21,330 of 35,345 by trust here, #27,551 by citations
• Computational Theory and Mathematics — 85 citations into it; #6,992 of 12,182 by trust here, #1,633 by citations
• Computational Mechanics — 83 citations into it
• Signal Processing — 81 citations into it; #945 of 7,572 by trust here, #918 by citations
• Computer Vision and Pattern Recognition — 57 citations into it
• Atomic and Molecular Physics, and Optics — 32 citations into it
• Numerical Methods & Scientific Computing — 22 citations into it

Whom Anh Huy Phan trusts overall

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

1. Tamara G. Kolda
2. Andrzej S. Cichocki
3. Ivan V. Oseledets
4. Evrim Acar
5. Lieven De Lathauwer
6. Rasmus Bro
7. Pierre Comon
8. Boris N. Khoromskij
9. Daniel M. Dunlavy
10. Brett W. Bader

Who trusts Anh Huy Phan overall

1. Andrzej S. Cichocki
2. Evangelos E. Papalexakis
3. Jingu Kim
4. Laura Gustafson
5. Petr Tichavský
6. Yu-Xiong Wang
7. Yangyang Xu
8. Haesun Park
9. Christos Faloutsos
10. Soheil Kolouri

Papers from Anh Huy Phan's perspective

Ordered by how strongly the researchers Anh Huy Phan 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Tensor Decompositions and Applications
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Nonnegative Matrix and Tensor Factorizations - Applications to Exploratory Multi-way Data Analysis and Blind Source Separation (2009) — 2,105 citations
• 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 Decompositions for Signal Processing Applications: From two-way to multiway component analysis (2014) — 1,400 citations
• On Fast Computation of Gradients for CANDECOMP/PARAFAC Algorithms (2012) — 18 citations
• Low Complexity Damped Gauss-Newton Algorithms for CANDECOMP/PARAFAC (2012) — 125 citations
• CANDECOMP/PARAFAC Decomposition of High-Order Tensors Through Tensor Reshaping (2012) — 62 citations
• Fast Local Algorithms for Large Scale Nonnegative Matrix and Tensor Factorizations (2009) — 607 citations