Emilio Carrizosa
Artificial Intelligence · 2001–2021
A researcher in the Science corpus: 19 papers; trusts 233 researchers and is trusted by 19 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 329 citations into it; #5,136 of 35,345 by trust here, #7,945 by citations
- Statistics and Probability — 102 citations into it
- Computer Vision and Pattern Recognition — 99 citations into it; #5,674 of 21,575 by trust here, #8,296 by citations
- Information Systems — 72 citations into it
- Statistical Learning Theory — 70 citations into it; #728 of 1,038 by trust here, #770 by citations
- Optimization & Training — 55 citations into it; #1,476 of 2,601 by trust here, #2,112 by citations
- Safety, Robustness & Interpretability — 50 citations into it
- Algorithms & Complexity — 43 citations into it; #331 of 516 by trust here, #365 by citations
Whom Emilio Carrizosa trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Vladimir Naumovich Vapni
- Trevor J. Hastie
- Yuhai Wu
- Robert Tibshirani
- Dolores Romero Morales
- Kristin P. Bennett
- Leo Breiman
- Vladimir Naumovich Vapnik
- J. Paul Brooks
- J. Ross Quinlan
Who trusts Emilio Carrizosa overall
- Berk Ustun
- Dolores Romero Morales
- Cynthia Rudin
- Laura Palagi
- Rafael Blanquero
- Amaya Nogales-Gómez
- Cristina Molero-Río
- Vinícius G. Costa
- Belén Martín-Barragán
- Sicco Verwer
Papers from Emilio Carrizosa's perspective
Ordered by how strongly the researchers Emilio Carrizosa trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Mathematical optimization in classification and regression trees (2021) — 137 citations
- On sparse optimal regression trees (2021) — 13 citations
- Optimal randomized classification trees (2021) — 52 citations
- On sparse ensemble methods: An application to short-term predictions of the evolution of COVID-19 (2021) — 19 citations
- On clustering categories of categorical predictors in generalized linear models (2021) — 18 citations
- Sparsity in Optimal Randomized Classification Trees (2020) — 51 citations
- Selection of time instants and intervals with Support Vector Regression for multivariate functional data (2020) — 12 citations
- Enhancing Interpretability in Factor Analysis by Means of Mathematical Optimization (2019) — 16 citations
- Functional-bandwidth kernel for Support Vector Machine with Functional Data: An alternating optimization algorithm (2019) — 20 citations
- On Building Online Visualization Maps for News Data Streams by Means of Mathematical Optimization (2018) — 5 citations
- Clustering Categories in Support Vector Machines (2017) — 31 citations
- Visualizing data as objects by DC (difference of convex) optimization (2017) — 24 citations
- Strongly agree or strongly disagree?: Rating features in Support Vector Machines (2016) — 29 citations
- A nested heuristic for parameter tuning in Support Vector Machines (2014) — 32 citations
- Supervised classification and mathematical optimization (2013) — 159 citations
- Time series interpolation via global optimization of moments fitting (2013) — 23 citations
- Binarized Support Vector Machines (2010) — 45 citations
- Multi-group support vector machines with measurement costs: A biobjective approach (2008) — 20 citations
- Gauge Distances and Median Hyperplanes (2001) — 49 citations