Marcelo Pereyra

Statistics and Probability · 2013–2016

A researcher in the Science corpus: 4 papers; trusts 128 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 122 citations into it; #3,060 of 12,932 by trust here, #5,294 by citations
• Artificial Intelligence — 95 citations into it; #34,589 of 35,345 by trust here, #25,040 by citations
• Probabilistic & Bayesian Methods — 60 citations into it
• Mathematical Physics — 57 citations into it
• Computational Mechanics — 51 citations into it; #2,129 of 10,001 by trust here, #7,501 by citations
• Optimization & Training — 32 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it
• Computational Theory and Mathematics — 20 citations into it

Whom Marcelo Pereyra trusts overall

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

1. Gareth O. Roberts
2. E.J. Candes
3. Patrick L. Combettes
4. Radford M. Neal
5. Venkat Chandrasekaran
6. Heinz H. Bauschke
7. Mark A. Girolami
8. A. Brix
9. Chris Hans
10. Richard L. Tweedie

Who trusts Marcelo Pereyra overall

1. Peter J. Green
2. Chris J. Oates
3. Raaz Dwivedi
4. K. Latuszy'nski
5. Mark A. Girolami
6. Arnak S. Dalalyan
7. Yuansi Chen
8. Nicolas Chopin
9. Christian P. Robert
10. Martin J. Wainwright

Papers from Marcelo Pereyra's perspective

Ordered by how strongly the researchers Marcelo Pereyra 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. ImageNet: A large-scale hierarchical image database
6. Learning Multiple Layers of Features from Tiny Images
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Regression Shrinkage and Selection via the Lasso
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Proximal Markov chain Monte Carlo algorithms (2013) — 201 citations
• Bayesian computation: a perspective on the current state, and sampling backwards and forwards (2015) — 22 citations
• Bayesian computation: a summary of the current state, and samples backwards and forwards (2015) — 197 citations
• Approximating Bayesian confidence regions in convex inverse problems (2016)