David S. Greenberg

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 28 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it; #26,211 of 35,345 by trust here, #29,412 by citations
• Probabilistic & Bayesian Methods — 35 citations into it
• Statistics and Probability — 26 citations into it
• Statistical and Nonlinear Physics — 11 citations into it; #7,152 of 10,127 by trust here, #8,120 by citations
• Generative Models — 8 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it

Whom David S. Greenberg trusts overall

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

1. George Papamakarios
2. Scott Anthony Sisson
3. MARK A. Beaumont
4. Michael G. B. Blum
5. Michael U Gutmann
6. Yanan Fan
7. Iain Murray
8. Simon N. Wood
9. Jan-Matthis Lueckmann
10. Paul Fearnhead

Who trusts David S. Greenberg overall

1. Joeri Hermans
2. J. Brehmer
3. George Papamakarios
4. Owen Thomas
5. Gilles Louppe
6. Volodimir Begy
7. Álvaro Tejero-Cantero
8. Eric T. Nalisnick
9. Ritabrata Dutta

Papers from David S. Greenberg's perspective

Ordered by how strongly the researchers David S. Greenberg trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet: A large-scale hierarchical image database
10. Auto-Encoding Variational Bayes (International Conference on Learning Representations)

Their papers

• Automatic Posterior Transformation for Likelihood-Free Inference (2019) — 457 citations
• sbi: A toolkit for simulation-based inference (2020) — 349 citations