Venkat Chandrasekaran
Computational Mechanics · 2008–2022
A researcher in the Science corpus: 7 papers; trusts 59 researchers and is trusted by 46 researchers here, as inferred from the citation graph.
Subfields
- Computational Mechanics — 46 citations into it; #1,351 of 10,001 by trust here, #598 by citations
- Nuclear and High Energy Physics — 34 citations into it; #4,776 of 6,161 by trust here, #5,861 by citations
- Astronomy and Astrophysics — 29 citations into it; #8,570 of 10,964 by trust here, #10,437 by citations
- Computational Theory and Mathematics — 25 citations into it
- Statistical and Nonlinear Physics — 23 citations into it
- Statistics and Probability — 23 citations into it; #3,740 of 12,932 by trust here, #3,177 by citations
- Numerical Analysis — 14 citations into it; #1,857 of 3,586 by trust here, #299 by citations
- Signal Processing — 14 citations into it
Whom Venkat Chandrasekaran trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- E.J. Candes
- David L. Donoho
- Johan Efberg
- Johan Löfberg
- Nicolai Meinshausen
- Martin J. Wainwright
- B. Recht
- Iain M. Johnstone
- Gitta Kutyniok
- Francis R. Bach
Who trusts Venkat Chandrasekaran overall
- Marcelo Pereyra
- Yudong Chen
- Martin J. Wainwright
- Martin Jaggi
- Alekh Agarwal
- Bin Dai
- Huan Xu
- Marten H. Wegkamp
- Junfeng Yang
- Sujay Sanghavi
Papers from Venkat Chandrasekaran's perspective
Ordered by how strongly the researchers Venkat Chandrasekaran trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Regression Shrinkage and Selection via the Lasso
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- 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)
- Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
- Long Short-Term Memory
- Learning Multiple Layers of Features from Tiny Images
Their papers
- The Convex Geometry of Linear Inverse Problems (2010) — 1,386 citations
- Latent variable graphical model selection via convex optimization (2010) — 543 citations
- Complexity of Inference in Graphical Models (2008) — 131 citations
- Diagonal and Low-Rank Matrix Decompositions, Correlation Matrices, and Ellipsoid Fitting (2012) — 83 citations
- An algebra of observables for de Sitter space (2022) — 305 citations
- Symmetries and charges of general relativity at null boundaries (2018) — 134 citations
- Rank-Sparsity Incoherence for Matrix Decomposition (2009) — 1,149 citations