John Thomas Ormerod
Statistics and Probability · 2007–2017
A researcher in the Science corpus: 11 papers; trusts 88 researchers and is trusted by 59 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 119 citations into it; #775 of 12,932 by trust here, #1,563 by citations
- Artificial Intelligence — 67 citations into it; #3,284 of 35,345 by trust here, #10,207 by citations
- Probabilistic & Bayesian Methods — 20 citations into it
- Computer Vision and Pattern Recognition — 15 citations into it
- Control and Systems Engineering — 15 citations into it
- Computational Mechanics — 11 citations into it; #4,311 of 10,001 by trust here, #6,287 by citations
- Management Science and Operations Research — 11 citations into it
- Environmental Engineering — 8 citations into it
Whom John Thomas Ormerod trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Matt P. Wand
- Jianqing Fan
- Radford M. Neal
- Robert Tibshirani
- Michael I. Jordan
- Eric R. Ziegel
- Jim Albert
- Tommi S. Jaakkola
- Trevor J. Hastie
- Jerome H. Friedman
Who trusts John Thomas Ormerod overall
- Matt P. Wand
- Linda S. L. Tan
- David Rohde
- Marianne Menictas
- Heera Lee
- David J. Nott
- Chong You
- David M. Blei
- A. Vergnes
- Emily K. Meineke
Papers from John Thomas Ormerod's perspective
Ordered by how strongly the researchers John Thomas Ormerod 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)
- Regression Shrinkage and Selection via the Lasso
- 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)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Mean field variational Bayes for continuous sparse signal shrinkage: Pitfalls and remedies (2014) — 67 citations
- On Variational Bayes Estimation and Variational Information Criteria for Linear Regression Models (2014) — 55 citations
- A variational Bayes approach to variable selection (2017) — 65 citations
- Computational Statistics and Data Analysis — 28 citations
- ON SEMIPARAMETRIC REGRESSION WITH O'SULLIVAN PENALIZED SPLINES (2008) — 209 citations
- Explaining Variational Approximations (2010) — 428 citations
- Mean field variational bayes for elaborate distributions (2011) — 189 citations
- Penalized Wavelets: Embedding Wavelets into Semiparametric Regression (2011) — 51 citations
- Extending the Linear Model With R: Generalized Linear, Mixed Effects and Nonparametric Regression Models (2007) — 902 citations
- Variational Approximations for Generalized Linear Latent Variable Models (2017) — 88 citations
- Theory of Gaussian variational approximation for a Poisson mixed model (2011) — 64 citations