Ben Calderhead

Statistics and Probability · 2008–2016

A researcher in the Science corpus: 6 papers; trusts 43 researchers and is trusted by 160 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 60 citations into it; #309 of 12,932 by trust here, #878 by citations
• Artificial Intelligence — 54 citations into it; #1,633 of 35,345 by trust here, #6,266 by citations
• Probabilistic & Bayesian Methods — 33 citations into it; #125 of 1,525 by trust here, #270 by citations
• Control and Systems Engineering — 12 citations into it
• Computational Theory and Mathematics — 11 citations into it
• Mathematical Physics — 11 citations into it
• Molecular Biology — 11 citations into it
• Statistical and Nonlinear Physics — 8 citations into it; #123 of 10,127 by trust here, #735 by citations

Whom Ben Calderhead trusts overall

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

1. Radford M. Neal
2. Andrew Gelman
3. Arnaud Doucet
4. Christophe Andrieu
5. Gareth O. Roberts
6. John K. Kruschke
7. Nial Friel
8. Vladislav Vyshemirsky
9. James M. Varah
10. Pierre Del Moral

Who trusts Ben Calderhead overall

1. Chris J. Oates
2. Theodore Papamarkou
3. Mark A. Girolami
4. Benn Macdonald
5. Chunyuan Li
6. Jianwen Xie
7. Michael Betancourt
8. Michalis K. Titsias
9. Ingmar Schuster
10. Zoubin Ghahramani

Papers from Ben Calderhead's perspective

Ordered by how strongly the researchers Ben Calderhead 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. Gradient-based learning applied to document recognition
5. Learning Multiple Layers of Features from Tiny Images
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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. Auto-Encoding Variational Bayes (International Conference on Learning Representations)

Their papers

• Riemann manifold Langevin and Hamiltonian Monte Carlo methods (2011) — 1,671 citations
• Riemannian Manifold Hamiltonian Monte Carlo (2009) — 40 citations
• Estimating Bayes factors via thermodynamic integration and population MCMC (2009) — 195 citations
• Bayesian Solution Uncertainty Quantification for Differential Equations (2013) — 135 citations
• Advances in Neural Information Processing Systems 29 (2016) — 155 citations
• Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes (2008) — 220 citations