Rafael Izbicki

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 4 papers; trusts 44 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 63 citations into it; #13,961 of 35,345 by trust here, #25,809 by citations
• Statistics and Probability — 40 citations into it; #5,021 of 12,932 by trust here, #8,279 by citations
• Computer Vision and Pattern Recognition — 21 citations into it
• Statistical Learning Theory — 15 citations into it
• Probabilistic & Bayesian Methods — 13 citations into it; #1,346 of 1,525 by trust here, #1,460 by citations
• Evaluation & Benchmarks — 8 citations into it
• Astronomy and Astrophysics — 7 citations into it
• Optimization & Training — 6 citations into it

Whom Rafael Izbicki trusts overall

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

1. Trevor J. Hastie
2. Alexander G. Gray
3. Stergios B. Fotopoulos
4. Robert Tibshirani
5. MARK A. Beaumont
6. T. Hayfield
7. Stéphane Mallat
8. Jeffrey S. Racine
9. Peter Hall
10. Samory Kpotufe

Who trusts Rafael Izbicki overall

1. Taylor Pospisil
2. Ann B. Lee
3. Edward H. Kennedy
4. Sivaraman Balakrishnan
5. Larry A. Wasserman

Papers from Rafael Izbicki's perspective

Ordered by how strongly the researchers Rafael Izbicki 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. Going deeper with convolutions
10. Et al

Their papers

• Converting High-Dimensional Regression to High-Dimensional Conditional Density Estimation (2017) — 93 citations
• ABC–CDE: Toward Approximate Bayesian Computation With Complex High-Dimensional Data and Limited Simulations (2018) — 37 citations
• Nonparametric Conditional Density Estimation in a High-Dimensional Regression Setting (2016) — 73 citations
• MeLIME: Meaningful Local Explanation for Machine Learning Models (2020) — 21 citations