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ORCIDRafael Cabañas
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2020 – today
- 2022
[c14]Luis A. Ortega, Rafael Cabañas, Andrés R. Masegosa:
Diversity and Generalization in Neural Network Ensembles. AISTATS 2022: 11720-11743- 2021
- [j8]Andrés R. Masegosa, Rafael Cabañas
, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón:
Probabilistic Models with Deep Neural Networks. Entropy 23(1): 117 (2021) - [j7]Manuel Gómez-Olmedo, Rafael Cabañas, Andrés Cano, Serafín Moral, Ofelia P. Retamero:
Value-based potentials: Exploiting quantitative information regularity patterns in probabilistic graphical models. Int. J. Intell. Syst. 36(11): 6913-6943 (2021) - [c13]Rafael Cabañas, Alessandro Antonucci:
CREPO: An Open Repository to Benchmark Credal Network Algorithms. ISIPTA 2021: 352-356 - [i7]Rafael Cabañas, Alessandro Antonucci:
CREPO: An Open Repository to Benchmark Credal Network Algorithms. CoRR abs/2105.04158 (2021) - [i6]Luis A. Ortega, Rafael Cabañas, Andrés R. Masegosa:
Diversity and Generalization in Neural Network Ensembles. CoRR abs/2110.13786 (2021) - 2020
- [j6]Javier Cózar, Rafael Cabañas, Antonio Salmerón, Andrés R. Masegosa:
InferPy: Probabilistic modeling with deep neural networks made easy. Neurocomputing 415: 408-410 (2020) - [c12]Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas:
Structural Causal Models Are (Solvable by) Credal Networks. PGM 2020: 581-592 - [c11]Rafael Cabañas, Alessandro Antonucci, David Huber, Marco Zaffalon:
CREDICI: A Java Library for Causal Inference by Credal Networks. PGM 2020: 597-600 - [c10]Rafael Cabañas, Javier Cózar, Antonio Salmerón, Andrés R. Masegosa:
Probabilistic Graphical Models with Neural Networks in InferPy. PGM 2020: 601-604 - [c9]David Huber, Rafael Cabañas, Alessandro Antonucci, Marco Zaffalon:
CREMA: A Java Library for Credal Network Inference. PGM 2020: 613-616 - [i5]Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas:
Structural Causal Models Are (Solvable by) Credal Networks. CoRR abs/2008.00463 (2020) - [i4]Marco Zaffalon, Alessandro Antonucci, Rafael Cabañas:
EM Based Bounding of Unidentifiable Queries in Structural Causal Models. CoRR abs/2011.02912 (2020)
2010 – 2019
- 2019
- [j5]Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Helge Langseth, Thomas D. Nielsen, Anders L. Madsen:
AMIDST: A Java toolbox for scalable probabilistic machine learning. Knowl. Based Syst. 163: 595-597 (2019) - [j4]Rafael Cabañas, Antonio Salmerón, Andrés R. Masegosa:
InferPy: Probabilistic modeling with Tensorflow made easy. Knowl. Based Syst. 168: 25-27 (2019) - [i3]Andrés R. Masegosa, Rafael Cabañas, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón:
Probabilistic Models with Deep Neural Networks. CoRR abs/1908.03442 (2019) - [i2]Javier Cózar, Rafael Cabañas, Andrés R. Masegosa, Antonio Salmerón:
InferPy: Probabilistic Modeling with Deep Neural Networks Made Easy. CoRR abs/1908.11161 (2019) - 2018
- [c8]Rafael Cabañas, Andrés Cano, Manuel Gómez-Olmedo, Andrés R. Masegosa, Serafín Moral:
Virtual Subconcept Drift Detection in Discrete Data Using Probabilistic Graphical Models. IPMU (3) 2018: 616-628 - 2017
- [j3]Rafael Cabañas, Alessandro Antonucci, Andrés Cano, Manuel Gómez-Olmedo:
Evaluating interval-valued influence diagrams. Int. J. Approx. Reason. 80: 393-411 (2017) - [i1]Andrés R. Masegosa, Ana M. Martínez, Darío Ramos-López, Rafael Cabañas, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen:
AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning. CoRR abs/1704.01427 (2017) - 2016
- [j2]Rafael Cabañas, Andrés Cano, Manuel Gómez-Olmedo, Anders L. Madsen:
Improvements to Variable Elimination and Symbolic Probabilistic Inference for evaluating Influence Diagrams. Int. J. Approx. Reason. 70: 13-35 (2016) - [j1]Rafael Cabañas, Manuel Gómez-Olmedo, Andrés Cano:
Using Binary Trees for the Evaluation of Influence Diagrams. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 24(1): 59-90 (2016) - [c7]Rafael Cabañas, Ana M. Martínez, Andrés R. Masegosa, Darío Ramos-López, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen:
Financial Data Analysis with PGMs Using AMIDST. ICDM Workshops 2016: 1284-1287 - 2015
- [c6]Rafael Cabañas, Alessandro Antonucci, Andrés Cano, Manuel Gómez-Olmedo:
Variable Elimination for Interval-Valued Influence Diagrams. ECSQARU 2015: 541-551 - 2014
- [c5]Rafael Cabañas, Anders L. Madsen, Andrés Cano, Manuel Gómez-Olmedo:
On SPI for Evaluating Influence Diagrams. IPMU (1) 2014: 506-516 - [c4]Rafael Cabañas, Andrés Cano, Manuel Gómez-Olmedo, Anders L. Madsen:
On SPI-Lazy Evaluation of Influence Diagrams. Probabilistic Graphical Models 2014: 97-112 - 2013
- [c3]Rafael Cabañas, Andrés Cano, Manuel Gómez-Olmedo, Anders L. Madsen:
Approximate Lazy Evaluation of Influence Diagrams. CAEPIA 2013: 321-331 - [c2]Rafael Cabañas, Manuel Gómez-Olmedo, Andrés Cano:
Evaluating Asymmetric Decision Problems with Binary Constraint Trees. ECSQARU 2013: 85-96 - [c1]Rafael Cabañas, Andrés Cano, Manuel Gómez-Olmedo, Anders L. Madsen:
Heuristics for Determining the Elimination Ordering in the Influence Diagram Evaluation with Binary Trees. SCAI 2013: 65-74
Coauthor Index
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