| AMIDST: A Java toolbox for scalable probabilistic machine learning AR Masegosa, AM Martínez, D Ramos-López, R Cabañas, A Salmerón, ... Knowledge-Based Systems 163, 595-597, 2019 | 21 | 2019 |
| Evaluating interval-valued influence diagrams R Cabañas, A Antonucci, A Cano, M Gómez-Olmedo International Journal of Approximate Reasoning 80, 393-411, 2017 | 12 | 2017 |
| Approximate inference in influence diagrams using binary trees RC de Paz, M Gómez-Olmedo, A Cano Proceedings of the 6th European Workshop on Probabilistic Graphical Models …, 2012 | 11 | 2012 |
| InferPy: Probabilistic modeling with TensorFlow made easy R Cabañas, A Salmerón, AR Masegosa Knowledge-Based Systems 168, 25-27, 2019 | 10 | 2019 |
| Structural causal models are (solvable by) credal networks M Zaffalon, A Antonucci, R Cabañas International Conference on Probabilistic Graphical Models, 581-592, 2020 | 8 | 2020 |
| Financial data analysis with PGMs using AMIDST R Cabañas, AM Martínez, AR Masegosa, D Ramos-López, A Samerón, ... 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW …, 2016 | 8 | 2016 |
| CREMA: a Java library for credal network inference D Huber, R Cabañas, A Antonucci, M Zaffalon International Conference on Probabilistic Graphical Models, 613-616, 2020 | 6 | 2020 |
| Virtual subconcept drift detection in discrete data using probabilistic graphical models R Cabañas, A Cano, M Gómez-Olmedo, AR Masegosa, S Moral International Conference on Information Processing and Management of …, 2018 | 5 | 2018 |
| Improvements to variable elimination and symbolic probabilistic inference for evaluating influence diagrams R Cabañas, A Cano, M Gómez-Olmedo, AL Madsen International Journal of Approximate Reasoning 70, 13-35, 2016 | 5 | 2016 |
| Using binary trees for the evaluation of influence diagrams R Cabanas, M Gomez-Olmedo, A Cano International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems …, 2016 | 5 | 2016 |
| On SPI for evaluating influence diagrams R Cabanas, AL Madsen, A Cano, M Gómez-Olmedo International Conference on Information Processing and Management of …, 2014 | 5 | 2014 |
| Probabilistic models with deep neural networks AR Masegosa, R Cabañas, H Langseth, TD Nielsen, A Salmerón Entropy 23 (1), 117, 2021 | 4 | 2021 |
| InferPy: Probabilistic Modeling with Deep Neural Networks Made Easy J Cózar, R Cabañas, A Salmerón, AR Masegosa Neurocomputing 415, 408, 2020 | 4 | 2020 |
| On SPI-lazy evaluation of influence diagrams R Cabañas, A Cano, M Gómez-Olmedo, AL Madsen European Workshop on Probabilistic Graphical Models, 97-112, 2014 | 4 | 2014 |
| Value-based potentials: exploiting quantitative information regularity patterns in probabilistic graphical models M Gómez-Olmedo, R Cabañas, A Cano, S Moral, OP Retamero International Journal of Intelligent Systems, 2021 | 3 | 2021 |
| Causal expectation-maximisation M Zaffalon, A Antonucci, R Cabañas arXiv preprint arXiv:2011.02912, 2020 | 3 | 2020 |
| Variable Elimination for Interval-Valued Influence Diagrams R Cabanas, A Antonucci, A Cano, M Gómez-Olmedo | 3 | 2015 |
| Diversity and Generalization in Neural Network Ensembles LA Ortega, R Cabañas, A Masegosa International Conference on Artificial Intelligence and Statistics, 11720-11743, 2022 | 2 | 2022 |
| CREDICI: A Java Library for Causal Inference by Credal Networks R Cabañas, A Antonucci, D Huber, M Zaffalon Proceedings of the 10th International Conference on Probabilistic Graphical …, 2020 | 2 | 2020 |
| Long-term survival in diffuse infiltrative brainstem gliomas in children and adolescents: treated with radiotherapy and Nimotuzumab J Alert, I Chon, R Cabanas, J Reno, D Garcia, M Perez, R Ropero Int J Radiol Radiat Ther 5 (4), 267-270, 2018 | 2 | 2018 |
Manuel Gómez-Olmedo (mgomez at d...Department of Computer Science and Artificial Intelligence, Universidad de GranadaVerified email at decsai.ugr.es
