| Deep learning for plant identification using vein morphological patterns GL Grinblat, LC Uzal, MG Larese, PM Granitto Computers and electronics in agriculture 127, 418-424, 2016 | 729 | 2016 |
| Toward large-scale vulnerability discovery using machine learning G Grieco, GL Grinblat, L Uzal, S Rawat, J Feist, L Mounier Proceedings of the sixth ACM conference on data and application security and …, 2016 | 317 | 2016 |
| Seed-per-pod estimation for plant breeding using deep learning LC Uzal, GL Grinblat, R Namías, MG Larese, JS Bianchi, EN Morandi, ... Computers and electronics in agriculture 150, 196-204, 2018 | 146 | 2018 |
| Solving nonstationary classification problems with coupled support vector machines GL Grinblat, LC Uzal, HA Ceccatto, PM Granitto IEEE Transactions on Neural Networks 22 (1), 37-51, 2011 | 62 | 2011 |
| Optimal reconstruction of dynamical systems: A noise amplification approach LC Uzal, GL Grinblat, PF Verdes Physical Review E 84 (1), 016223, 2011 | 51 | 2011 |
| Class-splitting generative adversarial networks GL Grinblat, LC Uzal, PM Granitto arXiv preprint arXiv:1709.07359, 2017 | 46 | 2017 |
| Abrupt change detection with one-class time-adaptive support vector machines GL Grinblat, LC Uzal, PM Granitto expert systems with applications 40 (18), 7242-7249, 2013 | 32 | 2013 |
| REPMAC: A new hybrid approach to highly imbalanced classification problems H Ahumada, GL Grinblat, LC Uzal, PM Granitto, A Ceccatto 2008 Eighth International Conference on Hybrid Intelligent Systems, 386-391, 2008 | 21 | 2008 |
| Predictions of the Maximum Amplitude, Time of Occurrence, and Total Length of Solar Cycle 24 LC Uzal, RD Piacentini, PF Verdes Solar Physics 279 (2), 551-560, 2012 | 18 | 2012 |
| Nonstationary regression with support vector machines GL Grinblat, LC Uzal, PF Verdes, PM Granitto Neural Computing and Applications 26 (3), 641-649, 2015 | 17 | 2015 |
| WGANVO: monocular visual odometry based on generative adversarial networks J Cremona, L Uzal, T Pire arXiv preprint arXiv:2007.13704, 2020 | 8 | 2020 |
| Redes neuronales adversarias para el reconocimiento de malezas JM Baruffaldi, L Uzal XX Concurso de Trabajos Estudiantiles-JAIIO 46 (Córdoba, 2017)., 2017 | 7 | 2017 |
| Exploiting gan internal capacity for high-quality reconstruction of natural images M Pividori, GL Grinblat, LC Uzal arXiv preprint arXiv:1911.05630, 2019 | 5 | 2019 |
| Análisis semántico en rostros utilizando redes neuronales profundas NF Pellejero, GL Grinblat, LC Uzal | 5 | 2017 |
| Semantic analysis on faces using deep neural networks NF Pellejero, G Grinblat, LC Uzal Inteligencia Artificial 21 (61), 14-29, 2018 | 3 | 2018 |
| WGANVO: odometría visual monocular basada en redes adversarias generativas JA Cremona, LC Uzal, TAN Pire Comité Español de Automática, 2021 | 2 | 2021 |
| Optimal Irregular Delay Embeddings LC Uzal, PF Verdes Dynamics Days South America 2010: International Conference on Chaos and …, 2010 | 2 | 2010 |
| Predicción de Sistemas Dinámicos con Redes Neuronales Profundas DG Maino Facultad de Ciencias Exactas, Ingeniería y Agrimensura. Universidad Nacional …, 2013 | 1 | 2013 |
| Evaluation of a new hybrid algorithm for highly imbalanced classification problems H Ahumada, GL Grinblat, LC Uzal, A Ceccatto, PM Granitto International Journal of Hybrid Intelligent Systems 8 (4), 199-211, 2011 | 1 | 2011 |
| Exploiting video sequences for unsupervised disentangling in generative adversarial networks F Tuesca, LC Uzal arXiv preprint arXiv:1910.11104, 2019 | | 2019 |
Guillermo Luis GrinblatEmail yang diverifikasi di cifasis-conicet.gov.ar