J. Brehmer

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 7 papers; trusts 73 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 128 citations into it; #19,153 of 35,345 by trust here, #15,236 by citations
• Probabilistic & Bayesian Methods — 110 citations into it; #1,057 of 1,525 by trust here, #832 by citations
• Statistics and Probability — 75 citations into it; #11,863 of 12,932 by trust here, #10,855 by citations
• Generative Models — 57 citations into it
• Nuclear and High Energy Physics — 55 citations into it; #2,474 of 6,161 by trust here, #3,560 by citations
• Computer Vision and Pattern Recognition — 53 citations into it; #19,809 of 21,575 by trust here, #20,271 by citations
• Astronomy and Astrophysics — 52 citations into it
• Statistical and Nonlinear Physics — 35 citations into it

Whom J. Brehmer trusts overall

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

1. Kyle Cranmer
2. Gilles Louppe
3. Ronald J. Williams
4. Laurent Dinh
5. Donald B. Rubin
6. Juan Pavez
7. Yoshua Bengio
8. Diederik P. Kingma
9. Geoffrey E. Hinton
10. George Papamakarios

Who trusts J. Brehmer overall

1. Kyle Cranmer
2. Gilles Louppe
3. George Papamakarios
4. Markus Stoye
5. Joeri Hermans
6. Justin Alsing
7. Siddharth Mishra-Sharma
8. Juan Pavez
9. Tom Charnock
10. Pablo de Castro

Papers from J. Brehmer's perspective

Ordered by how strongly the researchers J. Brehmer 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. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. GENERATIVE ADVERSARIAL NETS
10. Auto-Encoding Variational Bayes (International Conference on Learning Representations)

Their papers

• The frontier of simulation-based inference (2019) — 1,284 citations
• Likelihood-free inference with an improved cross-entropy estimator (2018) — 53 citations
• Handbook of LHC Higgs cross sections: 4. Deciphering the nature of the Higgs sector (2016) — 1,088 citations
• Mining for Dark Matter Substructure: Inferring Subhalo Population Properties from Strong Lenses with Machine Learning (2019) — 80 citations
• Constraining Effective Field Theories with Machine Learning. (2018) — 172 citations
• A guide to constraining effective field theories with machine learning (2018) — 157 citations
• Mining gold from implicit models to improve likelihood-free inference (2018) — 207 citations