Bart Vandereycken

Computational Theory and Mathematics · 2009–2020

A researcher in the Science corpus: 11 papers; trusts 94 researchers and is trusted by 46 researchers here, as inferred from the citation graph.

Subfields

• Computational Theory and Mathematics — 98 citations into it; #1,144 of 12,182 by trust here, #1,463 by citations
• Computational Mathematics — 76 citations into it; #106 of 612 by trust here, #130 by citations
• Computational Mechanics — 71 citations into it; #1,017 of 10,001 by trust here, #2,361 by citations
• Numerical Analysis — 43 citations into it; #645 of 3,586 by trust here, #1,448 by citations
• Artificial Intelligence — 36 citations into it
• Statistical and Nonlinear Physics — 32 citations into it; #1,863 of 10,127 by trust here, #5,625 by citations
• Atomic and Molecular Physics, and Optics — 30 citations into it
• Numerical Methods & Scientific Computing — 27 citations into it

Whom Bart Vandereycken trusts overall

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

1. Christian Lubich
2. Pierre-Antoine Absil
3. E.J. Candes
4. Prateek Jain
5. Othmar Koch
6. Valeria Simoncini
7. Ivan V. Oseledets
8. Daniel Kressner
9. Raghunandan H. Keshavan
10. Lars Grasedyck

Who trusts Bart Vandereycken overall

1. André Uschmajew
2. Valeria Simoncini
3. Daniel Kressner
4. Peter Benner
5. James Townsend
6. Pierre-Antoine Absil
7. Christian Lubich
8. Norman Lang
9. Andrzej S. Cichocki
10. Nicolas Boumal

Papers from Bart Vandereycken's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Tensor Decompositions and Applications
5. Gradient-based learning applied to document recognition
6. Regression Shrinkage and Selection via the Lasso
7. ImageNet: A large-scale hierarchical image database
8. The Geometry of Algorithms with Orthogonality Constraints
9. Long Short-Term Memory
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Dynamical Approximation by Hierarchical Tucker and Tensor-Train Tensors (2013) — 188 citations
• The geometry of algorithms using hierarchical tensors (2013) — 139 citations
• A Riemannian Optimization Approach for Computing Low-Rank Solutions of Lyapunov Equations (2010) — 127 citations
• Embedded geometry of the set of symmetric positive semidefinite matrices of fixed rank (2009) — 62 citations
• Unifying time evolution and optimization with matrix product states (2014) — 724 citations
• A Riemannian geometry with complete geodesics for the set of positive semidefinite matrices of fixed rank (2013) — 75 citations
• Geometric Methods on Low-Rank Matrix and Tensor Manifolds (2020) — 52 citations
• On critical points of quadratic low-rank matrix optimization problems (2020) — 20 citations
• Automatic rational approximation and linearization of nonlinear eigenvalue problems (2018) — 71 citations
• Low-rank tensor completion by Riemannian optimization (2014) — 344 citations
• Low-Rank Matrix Completion by Riemannian Optimization (2012) — 588 citations