Yaniv Plan
Computational Mechanics · 2008–2018
A researcher in the Science corpus: 9 papers; trusts 65 researchers and is trusted by 118 researchers here, as inferred from the citation graph.
Subfields
- Computational Mechanics — 103 citations into it; #243 of 10,001 by trust here, #476 by citations
- Statistics and Probability — 60 citations into it; #2,812 of 12,932 by trust here, #2,579 by citations
- Artificial Intelligence — 33 citations into it; #7,987 of 35,345 by trust here, #13,544 by citations
- Applied Mathematics — 28 citations into it
- Computer Networks and Communications — 25 citations into it; #2,641 of 9,886 by trust here, #4,128 by citations
- Signal Processing — 24 citations into it; #267 of 7,572 by trust here, #638 by citations
- Computer Vision and Pattern Recognition — 23 citations into it; #3,236 of 21,575 by trust here, #5,333 by citations
- Statistical Learning Theory — 21 citations into it; #169 of 1,038 by trust here, #249 by citations
Whom Yaniv Plan trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- E.J. Candes
- Terence Tao
- Michel Ledoux
- David L. Donoho
- Petros T. Boufounos
- Gitta Kutyniok
- Colin L. Mallows
- B. Recht
- Justin K. Romberg
- Roman Vershynin
Who trusts Yaniv Plan overall
- E.J. Candes
- Mark A. Davenport
- Raghunandan H. Keshavan
- Alexandre Belloni
- Arun K. Kuchibhotla
- David Gross
- Sourav Chatterjee
- Ainesh Bakshi
- Andrea Montanari
- Fadoua Balabdaoui
Papers from Yaniv Plan's perspective
Ordered by how strongly the researchers Yaniv Plan 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)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Atomic Decomposition by Basis Pursuit
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Matrix Completion With Noise (2009) — 1,855 citations
- Near-ideal model selection by ℓ1 minimization (2008) — 234 citations
- One‐Bit Compressed Sensing by Linear Programming (2011) — 441 citations
- 1-Bit Matrix Completion (2012) — 375 citations
- Tight Oracle Inequalities for Low-Rank Matrix Recovery From a Minimal Number of Noisy Random Measurements (2011) — 652 citations
- Global testing under sparse alternatives: ANOVA, multiple comparisons and the higher criticism (2010) — 223 citations
- A Probabilistic and RIPless Theory of Compressed Sensing (2010) — 599 citations
- Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes (2018) — 68 citations
- Robust 1-bit Compressed Sensing and Sparse Logistic Regression: A Convex Programming Approach (2012) — 478 citations