David C. Sterratt

Artificial Intelligence · 2018

A researcher in the Science corpus: 1 paper; trusts 55 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it; #11,762 of 35,345 by trust here, #19,873 by citations
• Probabilistic & Bayesian Methods — 30 citations into it; #735 of 1,525 by trust here, #945 by citations
• Statistics and Probability — 21 citations into it; #4,151 of 12,932 by trust here, #5,126 by citations
• Computer Vision and Pattern Recognition — 10 citations into it
• Generative Models — 7 citations into it; #747 of 1,956 by trust here, #1,435 by citations
• AI for Physical Sciences — 5 citations into it

Whom David C. Sterratt trusts overall

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

1. Radford M. Neal
2. Diederik P. Kingma
3. Aäron van den Oord
4. Edward Meeds
5. George Papamakarios
6. Max Welling
7. Geoffrey E. Hinton
8. Iain Murray
9. Scott Anthony Sisson
10. MARK A. Beaumont

Who trusts David C. Sterratt overall

1. Joeri Hermans
2. Justin Alsing
3. Conor Durkan
4. George Papamakarios
5. Gilles Louppe
6. David S. Greenberg
7. Volodimir Begy
8. Leah Price
9. Benjamin Dan Wandelt
10. Owen Thomas

Papers from David C. Sterratt's perspective

Ordered by how strongly the researchers David C. Sterratt 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows (2018) — 484 citations