Juan Pavez

Artificial Intelligence · 2015–2018

A researcher in the Science corpus: 5 papers; trusts 47 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it; #7,731 of 35,345 by trust here, #12,580 by citations
• Probabilistic & Bayesian Methods — 44 citations into it; #542 of 1,525 by trust here, #601 by citations
• Nuclear and High Energy Physics — 36 citations into it; #2,401 of 6,161 by trust here, #3,866 by citations
• Statistics and Probability — 36 citations into it; #10,508 of 12,932 by trust here, #10,855 by citations
• Computer Vision and Pattern Recognition — 27 citations into it; #17,461 of 21,575 by trust here, #20,271 by citations
• Generative Models — 26 citations into it
• Statistical and Nonlinear Physics — 18 citations into it
• Astronomy and Astrophysics — 9 citations into it

Whom Juan Pavez trusts overall

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

1. Kyle Cranmer
2. Radford M. Neal
3. Ronald J. Williams
4. Gilles Louppe
5. Lotfi A. Zadeh
6. The Cms Collaboration
7. J. Brehmer
8. Laurent Dinh
9. Masashi Sugiyama
10. Geoffrey E. Hinton

Who trusts Juan Pavez overall

1. J. Brehmer
2. Gilles Louppe
3. Kyle Cranmer
4. Joeri Hermans
5. George Papamakarios
6. Markus Stoye
7. Pablo de Castro
8. Volodimir Begy
9. Traiko Dinev
10. Justin Alsing

Papers from Juan Pavez's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. ImageNet: A large-scale hierarchical image database

Their papers

• Approximating Likelihood Ratios with Calibrated Discriminative Classifiers (2015) — 273 citations
• Likelihood-free inference with an improved cross-entropy estimator (2018) — 53 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