Dennis Prangle
Statistics and Probability · 2012–2018
A researcher in the Science corpus: 3 papers; trusts 53 researchers and is trusted by 46 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 54 citations into it; #1,359 of 12,932 by trust here, #2,023 by citations
- Artificial Intelligence — 48 citations into it; #4,755 of 35,345 by trust here, #10,566 by citations
- Probabilistic & Bayesian Methods — 21 citations into it; #1,412 of 1,525 by trust here, #1,365 by citations
- Genetics — 7 citations into it
- Molecular Biology — 6 citations into it
- Analytical Chemistry — 5 citations into it
- Applied Mathematics — 5 citations into it
- Mathematical Physics — 5 citations into it
Whom Dennis Prangle trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- MARK A. Beaumont
- Paul Marjoram
- Scott Anthony Sisson
- Michael G. B. Blum
- Paul Joyce
- Simon N. Wood
- Ker-Chau Li
- Knut Heggland
- Arnaud Doucet
- Daniel Wegmann
Who trusts Dennis Prangle overall
- Traiko Dinev
- Michael U Gutmann
- David S. Greenberg
- Yanan Fan
- Mikael Sunnåker
- Peter J. Green
- S. Aeschbacher
- Bai Jiang
- Shigeki Nakagome
- Stuart Barber
Papers from Dennis Prangle's perspective
Ordered by how strongly the researchers Dennis Prangle trusts endorse each paper. Same corpus, a different order for every researcher.
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Auto-Encoding Variational Bayes (International Conference on Learning Representations)
- Markov chain Monte Carlo without likelihoods
- Sequential Monte Carlo without likelihoods
- Stochastic Backpropagation and Approximate Inference in Deep Generative Models
- ImageNet classification with deep convolutional neural networks
- Gradient-based learning applied to document recognition
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Black-Box Variational Inference for Stochastic Differential Equations (2018) — 74 citations
- Constructing summary statistics for approximate Bayesian computation: semi‐automatic approximate Bayesian computation (2012) — 629 citations
- A comparative review of dimension reduction methods in approximate Bayesian computation (2012) — 376 citations