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Ricardo Ñanculef
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2020 – today
- 2024
- [j11]Manuel Alejandro Goyo, Ricardo Ñanculef, Carlos Valle:
InverseTime: A Self-Supervised Technique for Semi-Supervised Classification of Time Series. IEEE Access 12: 165081-165093 (2024) - [j10]Mauricio Solar, Victor Castañeda, Ricardo Ñanculef, Lioubov Dombrovskaia, Mauricio Araya:
A Data Ingestion Procedure towards a Medical Images Repository. Sensors 24(15): 4985 (2024) - [c35]Sebastián Jara, Rodrigo Salas, Ricardo Ñanculef, Israel Valverde, Sergio Uribe, Julio Sotelo:
Prediction of Peak-to-Peak Pressure Gradient in Patients with Aortic Coarctation Using Physics-Informed Neural Networks. CLEI 2024: 1-4 - [c34]Mauricio Solar, Mauricio Araya, Ricardo Ñanculef, Lioubov Dombrovskaia, Victor Castañeda, Ian Roberts:
ALPACS: Interoperable Repository of Medical Images. CLEI 2024: 1-9 - [c33]Mario Mallea, Ricardo Ñanculef, Denis Parra:
Adversarial Pairwise Multimodal Recommendation. IJCNN 2024: 1-10 - [d1]Domingo Benoit, Ricardo Ñanculef:
Hate Speech in Chilean Twitter. IEEE DataPort, 2024 - 2023
- [j9]Iván Pizarro, Ricardo Ñanculef, Carlos Valle:
An Attention-Based Architecture for Hierarchical Classification With CNNs. IEEE Access 11: 32972-32995 (2023) - [c32]Gonzalo Rivera Lazo, Hernán Astudillo, Ricardo Ñanculef:
Attention Mechanisms in Process Mining: A Systematic Literature Review. CLEI 2023: 1-10 - [c31]Mario Mallea, Ricardo Ñanculef, Mauricio Araya:
Enhancing Intra-modal Similarity in a Cross-Modal Triplet Loss. DS 2023: 249-264 - 2022
- [c30]Gonzalo Rivera Lazo, Ricardo Ñanculef:
Multi-attribute Transformers for Sequence Prediction in Business Process Management. DS 2022: 184-194 - [c29]Manuel Alejandro Goyo, Ricardo Ñanculef:
Cluster Distillation: Semi-supervised Time Series Classification through Clustering-based Self-supervision. SCCC 2022: 1-8 - [i9]Francisco Andrades, Ricardo Ñanculef:
A Method to Predict Semantic Relations on Artificial Intelligence Papers. CoRR abs/2201.10518 (2022) - 2021
- [c28]Francisco Andrades, Ricardo Ñanculef:
A Method to Predict Semantic Relations on Artificial Intelligence Papers. IEEE BigData 2021: 5795-5800 - [c27]Ricardo Ñanculef, Francisco Alejandro Mena, Antonio Macaluso, Stefano Lodi, Claudio Sartori:
Self-supervised Bernoulli Autoencoders for Semi-supervised Hashing. CIARP 2021: 258-268 - 2020
- [j8]Franklin Johnson, Alvaro Valderrama, Carlos Valle, Broderick Crawford, Ricardo Soto, Ricardo Ñanculef:
Automating Configuration of Convolutional Neural Network Hyperparameters Using Genetic Algorithm. IEEE Access 8: 156139-156152 (2020) - [i8]Ricardo Ñanculef, Francisco Alejandro Mena, Antonio Macaluso, Stefano Lodi, Claudio Sartori:
Self-Supervised Bernoulli Autoencoders for Semi-Supervised Hashing. CoRR abs/2007.08799 (2020)
2010 – 2019
- 2019
- [j7]Carlos Valle, Ricardo Ñanculef, Héctor Allende, Claudio Moraga:
LocalBoost: A Parallelizable Approach to Boosting Classifiers. Neural Process. Lett. 50(1): 19-41 (2019) - [c26]Francisco Alejandro Mena, Ricardo Ñanculef:
A Binary Variational Autoencoder for Hashing. CIARP 2019: 131-141 - [c25]Francisco Alejandro Mena, Ricardo Ñanculef:
Revisiting Machine Learning from Crowds a Mixture Model for Grouping Annotations. CIARP 2019: 493-503 - [c24]Rafik Mas'Ad, Ricardo Ñanculef, Hernán Astudillo:
BlackSheep: Dynamic Effort Estimation in Agile Software Development using Machine Learning. CIbSE 2019: 16-29 - [i7]Francisco Alejandro Mena, Ricardo Ñanculef:
Evaluating Bregman Divergences for Probability Learning from Crowd. CoRR abs/1901.10653 (2019) - 2018
- [c23]Cristian M. Orellana, Ricardo Ñanculef, Carlos Valle:
Boosting Collaborative Filters for Drug-Target Interaction Prediction. CIARP 2018: 212-220 - [c22]Simone Balocco, Mauricio González, Ricardo Ñanculef, Petia Radeva, Gabriel Thomas:
Calcified Plaque Detection in IVUS Sequences: Preliminary Results Using Convolutional Nets. IWAIPR 2018: 34-42 - 2016
- [j6]Emanuele Frandi, Ricardo Ñanculef, Stefano Lodi, Claudio Sartori, Johan A. K. Suykens:
Fast and scalable Lasso via stochastic Frank-Wolfe methods with a convergence guarantee. Mach. Learn. 104(2-3): 195-221 (2016) - [c21]Julio Hurtado, Marcelo Mendoza, Ricardo Ñanculef:
Boosting SpLSA for Text Classification. CIARP 2016: 142-149 - [c20]Marcelo Aliquintuy, Emanuele Frandi, Ricardo Ñanculef, Johan A. K. Suykens:
Efficient Sparse Approximation of Support Vector Machines Solving a Kernel Lasso. CIARP 2016: 208-216 - [i6]Ricardo Ñanculef, Ilias N. Flaounas, Nello Cristianini:
Efficient Classification of Multi-Labelled Text Streams by Clashing. CoRR abs/1604.03200 (2016) - 2015
- [c19]Emanuele Frandi, Ricardo Ñanculef, Johan A. K. Suykens:
A PARTAN-accelerated Frank-Wolfe algorithm for large-scale SVM classification. IJCNN 2015: 1-8 - [i5]Emanuele Frandi, Ricardo Ñanculef, Johan A. K. Suykens:
A PARTAN-Accelerated Frank-Wolfe Algorithm for Large-Scale SVM Classification. CoRR abs/1502.01563 (2015) - [i4]Emanuele Frandi, Ricardo Ñanculef, Stefano Lodi, Claudio Sartori, Johan A. K. Suykens:
Fast and Scalable Lasso via Stochastic Frank-Wolfe Methods with a Convergence Guarantee. CoRR abs/1510.07169 (2015) - 2014
- [j5]Ricardo Ñanculef, Ilias N. Flaounas, Nello Cristianini:
Efficient classification of multi-labeled text streams by clashing. Expert Syst. Appl. 41(11): 5431-5450 (2014) - [j4]Ricardo Ñanculef, Emanuele Frandi, Claudio Sartori, Héctor Allende:
A novel Frank-Wolfe algorithm. Analysis and applications to large-scale SVM training. Inf. Sci. 285: 66-99 (2014) - [i3]Emanuele Frandi, Ricardo Ñanculef, Johan A. K. Suykens:
Complexity Issues and Randomization Strategies in Frank-Wolfe Algorithms for Machine Learning. CoRR abs/1410.4062 (2014) - 2013
- [j3]Emanuele Frandi, Ricardo Ñanculef, Maria Grazia Gasparo, Stefano Lodi, Claudio Sartori:
Training Support Vector Machines using Frank-Wolfe Optimization Methods. Int. J. Pattern Recognit. Artif. Intell. 27(3) (2013) - [i2]Héctor Allende, Emanuele Frandi, Ricardo Ñanculef, Claudio Sartori:
Novel Frank-Wolfe Methods for SVM Learning. CoRR abs/1304.1014 (2013) - 2012
- [j2]Ricardo Ñanculef, Carlos Valle, Héctor Allende, Claudio Moraga:
Training regression ensembles by sequential target correction and resampling. Inf. Sci. 195: 154-174 (2012) - [i1]Emanuele Frandi, Ricardo Ñanculef, Maria Grazia Gasparo, Stefano Lodi, Claudio Sartori:
Training Support Vector Machines Using Frank-Wolfe Optimization Methods. CoRR abs/1212.0695 (2012) - 2011
- [c18]Ricardo Ñanculef, Erick López, Héctor Allende, Héctor Allende-Cid:
An Ensemble Method for Incremental Classification in Stationary and Non-stationary Environments. CIARP 2011: 541-548 - [c17]Ricardo Ñanculef, Héctor Allende, Stefano Lodi, Claudio Sartori:
Two One-Pass Algorithms for Data Stream Classification Using Approximate MEBs. ICANNGA (2) 2011: 363-372 - 2010
- [c16]Emanuele Frandi, Maria Grazia Gasparo, Stefano Lodi, Ricardo Ñanculef, Claudio Sartori:
A New Algorithm for Training SVMs Using Approximate Minimal Enclosing Balls. CIARP 2010: 87-95 - [c15]Diego Candel, Ricardo Ñanculef, Carlos Concha, Héctor Allende:
A Sequential Minimal Optimization Algorithm for the All-Distances Support Vector Machine. CIARP 2010: 484-491 - [c14]Stefano Lodi, Ricardo Ñanculef, Claudio Sartori:
Single-Pass Distributed Learning of Multi-class SVMs Using Core-Sets. SDM 2010: 257-268 - [c13]Stefano Lodi, Ricardo Ñanculef, Claudio Sartori:
Learning Multi-Class Support Vector Models from Distributed Data using Core-Sets (Extended Abstract). SEBD 2010: 150-157
2000 – 2009
- 2009
- [j1]Ricardo Ñanculef, Carlos Concha, Héctor Allende, Diego Candel, Claudio Moraga:
AD-SVMs: A light extension of SVMs for multicategory classification. Int. J. Hybrid Intell. Syst. 6(2): 69-79 (2009) - [c12]Stefano Lodi, Ricardo Ñanculef, Claudio Sartori:
L2-SVM Training with Distributed Data. MATES 2009: 208-213 - 2008
- [c11]Ricardo Ñanculef, Carlos Concha, Héctor Allende, Diego Candel, Claudio Moraga:
Multicategory SVMs by Minimizing the Distances among Convex-Hull Prototypes. HIS 2008: 423-428 - 2007
- [c10]Héctor Allende-Cid, Rodrigo Salas, Héctor Allende, Ricardo Ñanculef:
Robust Alternating AdaBoost. CIARP 2007: 427-436 - [c9]Ricardo Ñanculef, Carlos Valle, Héctor Allende, Claudio Moraga:
Bagging with Asymmetric Costs for Misclassified and Correctly Classified Examples. CIARP 2007: 694-703 - [c8]Carlos Valle, Ricardo Ñanculef, Héctor Allende, Claudio Moraga:
Two Bagging Algorithms with Coupled Learners to Encourage Diversity. IDA 2007: 130-139 - [c7]Patricia Trejo, Ricardo Ñanculef, Héctor Allende, Claudio Moraga:
Probabilistic Aggregation of Classifiers for Incremental Learning. IWANN 2007: 135-143 - 2006
- [c6]Ricardo Ñanculef, Carlos Valle, Héctor Allende, Claudio Moraga:
Ensemble Learning with Local Diversity. ICANN (1) 2006: 264-273 - [c5]Ricardo Ñanculef, Carlos Valle, Héctor Allende, Claudio Moraga:
Local Negative Correlation with Resampling. IDEAL 2006: 570-577 - 2005
- [c4]Ricardo Ñanculef, Carlos Valle, Héctor Allende, Claudio Moraga:
Moderated Innovations in Self-poised Ensemble Learning. CIS (1) 2005: 49-56 - [c3]Ricardo Ñanculef, Carlos Valle, Héctor Allende, Claudio Moraga:
Self-poised Ensemble Learning. IDA 2005: 272-282 - 2004
- [c2]Ricardo Ñanculef, Carlos Concha, Claudio Moraga, Héctor Allende:
Multiresolution Fuzzy Rule Systems. Fuzzy Days 2004: 65-79 - [c1]Héctor Allende, Ricardo Ñanculef, Rodrigo Salas:
Robust Bootstrapping Neural Networks. MICAI 2004: 813-822
Coauthor Index
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last updated on 2024-12-02 21:32 CET by the dblp team
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