Bang Công Vu
Optimization & Training · 2010–2017
A researcher in the Science corpus: 6 papers; trusts 40 researchers and is trusted by 37 researchers here, as inferred from the citation graph.
Subfields
- Optimization & Training — 39 citations into it; #457 of 2,601 by trust here, #1,365 by citations
- Computational Mechanics — 29 citations into it; #4,437 of 10,001 by trust here, #6,582 by citations
- Computational Theory and Mathematics — 26 citations into it; #578 of 12,182 by trust here, #2,565 by citations
- Artificial Intelligence — 21 citations into it; #19,798 of 35,345 by trust here, #30,347 by citations
- Mathematical Optimization — 17 citations into it
- Mathematical Physics — 15 citations into it; #500 of 8,562 by trust here, #1,515 by citations
- Statistics and Probability — 14 citations into it; #1,139 of 12,932 by trust here, #3,609 by citations
- Numerical Analysis — 12 citations into it; #1,499 of 3,586 by trust here, #1,166 by citations
Whom Bang Công Vu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Patrick L. Combettes
- Heinz H. Bauschke
- Bingsheng He
- John C. Duchi
- Yurii E. Nesterov
- Anatoli B. Juditsky
- Paul Tseng
- M. Solodov
- Lin Xiao
- Xiaoming Yuan
Who trusts Bang Công Vu overall
- Laurent Condat
- Ming Yan
- Luis M. Briceño-Arias
- Radu Ioan Bot
- Adil Salim
- Carlo Ciliberto
- Pascal Bianchi
- Patrick L. Combettes
- Peter Richtárik
- S. Chraibi
Papers from Bang Công Vu's perspective
Ordered by how strongly the researchers Bang Công Vu trusts endorse each paper. Same corpus, a different order for every researcher.
- Regression Shrinkage and Selection via the Lasso
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- ImageNet classification with deep convolutional neural networks
- A Stochastic Approximation Method
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Gradient methods for minimizing composite objective function
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- On the linear convergence of the stochastic gradient method with constant step-size (2017) — 31 citations
- A splitting algorithm for dual monotone inclusions involving cocoercive operators (2011) — 623 citations
- A Stochastic forward-backward splitting method for solving monotone inclusions in Hilbert spaces (2014) — 19 citations
- Variable metric forward–backward splitting with applications to monotone inclusions in duality (2012) — 215 citations
- On the linear convergence of the projected stochastic gradient method with constant step-size (2017)
- Proximity for sums of composite functions (2010) — 70 citations