Jon Cockayne

Statistics and Probability · 2016–2017

A researcher in the Science corpus: 3 papers; trusts 27 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 45 citations into it; #6,348 of 12,932 by trust here, #6,853 by citations
• Artificial Intelligence — 38 citations into it; #17,388 of 35,345 by trust here, #24,417 by citations
• Probabilistic & Bayesian Methods — 22 citations into it
• Statistical and Nonlinear Physics — 17 citations into it
• Numerical Analysis — 12 citations into it
• Statistical Learning Theory — 10 citations into it
• Numerical Methods & Scientific Computing — 10 citations into it
• Management Science and Operations Research — 7 citations into it

Whom Jon Cockayne trusts overall

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

1. Andrew M. Stuart
2. Philipp Hennig
3. Masoumeh Dashti
4. Mark A. Girolami
5. Chris J. Oates
6. David John Cameron MacKay
7. Pierre Del Moral
8. Andrew Gelman
9. James O. Berger
10. Persi Diaconis

Who trusts Jon Cockayne overall

1. Yibo Yang
2. Paris Perdikaris
3. Maizar Raissi
4. Jackson Gorham
5. Chris J. Oates
6. G. Em Karniadakis

Papers from Jon Cockayne's perspective

Ordered by how strongly the researchers Jon Cockayne 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. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Probabilistic Meshless Methods for Partial Differential Equations and Bayesian Inverse Problems (2016) — 47 citations
• Convergence rates for a class of estimators based on Stein’s method (2016) — 61 citations
• Bayesian Probabilistic Numerical Methods (2017) — 182 citations