Adel Javanmard
Statistics and Probability · 2013–2020
A researcher in the Science corpus: 5 papers; trusts 92 researchers and is trusted by 108 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 92 citations into it; #371 of 12,932 by trust here, #1,057 by citations
- Artificial Intelligence — 82 citations into it; #1,821 of 35,345 by trust here, #4,420 by citations
- Computational Mechanics — 58 citations into it; #1,259 of 10,001 by trust here, #3,908 by citations
- Statistical Learning Theory — 37 citations into it
- Optimization & Training — 21 citations into it
- Computer Vision and Pattern Recognition — 19 citations into it; #5,533 of 21,575 by trust here, #6,671 by citations
- Safety, Robustness & Interpretability — 18 citations into it
- Signal Processing — 18 citations into it
Whom Adel Javanmard trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David L. Donoho
- E.J. Candes
- Peter J. Bickel
- Cun-Hui Zhang
- Roman Vershynin
- Tingni Sun
- Sara A. van de Geer
- Robert Tibshirani
- Peter Buhlmann
- Nicolai Meinshausen
Who trusts Adel Javanmard overall
- Victor Chernozhukov
- Jana Jankov'a
- Yinchu Zhu
- Sara A. van de Geer
- Jason D. Lee
- Jelena Bradic
- Whitney K. Newey
- Jonathan E. Taylor
- Yang Ning
- Vira Semenova
Papers from Adel Javanmard's perspective
Ordered by how strongly the researchers Adel Javanmard trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- 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
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Regression Shrinkage and Selection via the Lasso
Their papers
- Theoretical Insights Into the Optimization Landscape of Over-Parameterized Shallow Neural Networks (2017) — 450 citations
- Confidence intervals and hypothesis testing for high-dimensional regression (2013) — 832 citations
- Hypothesis Testing in High-Dimensional Regression Under the Gaussian Random Design Model: Asymptotic Theory (2013) — 167 citations
- Debiasing the lasso: Optimal sample size for Gaussian designs (2015) — 207 citations
- Precise Tradeoffs in Adversarial Training for Linear Regression (2020) — 124 citations