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2020 – today
- 2024
[i14]Juan Pablo Mesa, Alejandro Montoya, Raúl Ramos-Pollán, Mauricio Toro:
A Bi-Objective Approach to Last-Mile Delivery Routing Considering Driver Preferences. CoRR abs/2405.16051 (2024)- [i13]Matthew J. Allen, Francisco Dorr, Joseph Alejandro Gallego-Mejia, Laura Martínez-Ferrer, Anna Jungbluth, Freddie Kalaitzis, Raúl Ramos-Pollán:
M3LEO: A Multi-Modal, Multi-Label Earth Observation Dataset Integrating Interferometric SAR and RGB Data. CoRR abs/2406.04230 (2024) - [i12]Raúl Ramos-Pollán, Freddie Kalaitzis, Karthick Panner Selvam:
Uncertainty and Generalizability in Foundation Models for Earth Observation. CoRR abs/2409.08744 (2024) - [i11]Karthick Panner Selvam, Raúl Ramos-Pollán, Freddie Kalaitzis:
Rapid Adaptation of Earth Observation Foundation Models for Segmentation. CoRR abs/2409.09907 (2024) - 2023
- [j10]Juan Pablo Mesa
, Alejandro Montoya, Raúl Ramos-Pollán, Mauricio Toro:
A two-stage data-driven metaheuristic to predict last-mile delivery route sequences. Eng. Appl. Artif. Intell. 125: 106653 (2023) - [d5]Raúl Ramos-Pollán, Fabio A. González:
Sentinel2 RGB chips over BENELUX with ESA World Cover for Learning with Label Proportions. Zenodo, 2023
- [d4]Raúl Ramos-Pollán, Fabio A. González:
Sentinel2 RGB chips over Colombia (NE) with ESA World Cover for Learning with Label Proportions. Zenodo, 2023 - [d3]Raúl Ramos-Pollán, Fabio A. González:
Sentinel2 RGB chips over Colombia (NE) with JRC GHSL Population Density 2015 for Learning with Label Proportions. Zenodo, 2023 - [d2]Raúl Ramos-Pollán, Fabio A. González:
Sentinel2 RGB chips over BENELUX with JRC GHSL Population Density 2015 for Learning with Label Proportions. Zenodo, 2023 - [d1]Raúl Ramos-Pollán, Fabio Augusto González Osorio:
Learning with Label Proportions on Sentinel-2 RGB imagery. IEEE DataPort, 2023 - [i10]Omar A. Castaño-Idarraga, Raúl Ramos-Pollán, Freddie Kalaitzis:
A Contrastive Method Based on Elevation Data for Remote Sensing with Scarce and High Level Semantic Labels. CoRR abs/2304.06857 (2023) - [i9]Fabio A. González, Raúl Ramos-Pollán, Joseph A. Gallego-Mejia:
Quantum Kernel Mixtures for Probabilistic Deep Learning. CoRR abs/2305.18204 (2023) - [i8]Raúl Ramos-Pollán, Fabio A. González:
Lightweight learning from label proportions on satellite imagery. CoRR abs/2306.12461 (2023) - [i7]Matt Allen, Francisco Dorr, Joseph A. Gallego-Mejia, Laura Martínez-Ferrer, Anna Jungbluth, Freddie Kalaitzis, Raúl Ramos-Pollán:
Fewshot learning on global multimodal embeddings for earth observation tasks. CoRR abs/2310.00119 (2023) - [i6]Matt Allen, Francisco Dorr, Joseph A. Gallego-Mejia, Laura Martínez-Ferrer, Anna Jungbluth, Freddie Kalaitzis, Raúl Ramos-Pollán:
Large Scale Masked Autoencoding for Reducing Label Requirements on SAR Data. CoRR abs/2310.00826 (2023) - [i5]Laura Martínez-Ferrer, Anna Jungbluth, Joseph A. Gallego-Mejia, Matt Allen, Francisco Dorr, Freddie Kalaitzis, Raúl Ramos-Pollán:
Exploring Generalisability of Self-Distillation with No Labels for SAR-Based Vegetation Prediction. CoRR abs/2310.02048 (2023) - [i4]Joseph A. Gallego-Mejia, Anna Jungbluth, Laura Martínez-Ferrer, Matt Allen, Francisco Dorr, Freddie Kalaitzis, Raúl Ramos-Pollán:
Exploring DINO: Emergent Properties and Limitations for Synthetic Aperture Radar Imagery. CoRR abs/2310.03513 (2023) - 2022
- [c13]Miguel Plazas, Raúl Ramos-Pollán, Fabian León, Fabio Martínez:
Towards reduction of expert bias on Gleason score classification via a semi-supervised deep learning strategy. Medical Imaging: Image Processing 2022 - [i3]Vanessa Böhm, Wei Ji Leong, Ragini Bal Mahesh, Ioannis Prapas, Edoardo Nemni, Freddie Kalaitzis, Siddha Ganju, Raúl Ramos-Pollán:
Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes. CoRR abs/2211.02869 (2022) - [i2]Vanessa Böhm, Wei Ji Leong, Ragini Bal Mahesh, Ioannis Prapas, Edoardo Nemni, Freddie Kalaitzis, Siddha Ganju, Raúl Ramos-Pollán:
SAR-based landslide classification pretraining leads to better segmentation. CoRR abs/2211.09927 (2022) - [i1]Vanessa Boehm, Wei Ji Leong, Ragini Bal Mahesh, Ioannis Prapas, Edoardo Nemni, Freddie Kalaitzis, Siddha Ganju, Raúl Ramos-Pollán:
Deep learning based landslide density estimation on SAR data for rapid response. CoRR abs/2211.10338 (2022) - 2021
- [j9]Reinel Tabares-Soto, Harold Brayan Arteaga-Arteaga, Mario Alejandro Bravo-Ortiz, Alejandro Mora-Rubio, Daniel Arias-Garzón, Jesús Alejandro Alzate-Grisales, Alejandro Buenaventura Burbano-Jacome, Simon Orozco-Arias, Gustavo A. Isaza, Raúl Ramos-Pollán:
GBRAS-Net: A Convolutional Neural Network Architecture for Spatial Image Steganalysis. IEEE Access 9: 14340-14350 (2021) - [j8]Reinel Tabares-Soto, Harold Brayan Arteaga-Arteaga, Alejandro Mora-Rubio, Mario Alejandro Bravo-Ortiz, Daniel Arias-Garzón, Jesús Alejandro Alzate-Grisales, Alejandro Buenaventura Burbano-Jacome, Simon Orozco-Arias, Gustavo A. Isaza, Raúl Ramos-Pollán:
Strategy to improve the accuracy of convolutional neural network architectures applied to digital image steganalysis in the spatial domain. PeerJ Comput. Sci. 7: e451 (2021) - [j7]Reinel Tabares-Soto, Harold Brayan Arteaga-Arteaga, Alejandro Mora-Rubio, Mario Alejandro Bravo-Ortiz, Daniel Arias-Garzón, Jesús Alejandro Alzate-Grisales, Simon Orozco-Arias, Gustavo A. Isaza, Raúl Ramos-Pollán:
Sensitivity of deep learning applied to spatial image steganalysis. PeerJ Comput. Sci. 7: e616 (2021)
2010 – 2019
- 2019
- [j6]Reinel Tabares-Soto, Raúl Ramos-Pollán, Gustavo A. Isaza:
Deep Learning Applied to Steganalysis of Digital Images: A Systematic Review. IEEE Access 7: 68970-68990 (2019) - [e1]Esteban Meneses, Harold Castro, Carlos Jaime Barrios Hernández, Raúl Ramos-Pollán:
High Performance Computing - 5th Latin American Conference, CARLA 2018, Bucaramanga, Colombia, September 26-28, 2018, Revised Selected Papers. Communications in Computer and Information Science 979, Springer 2019, ISBN 978-3-030-16204-7 [contents] - 2017
- [j5]Diego Rueda-Plata, Raúl Ramos-Pollán, Fabio A. González:
Effective training of convolutional neural networks with small, specialized datasets. J. Intell. Fuzzy Syst. 32(2): 1333-1342 (2017) - 2016
- [j4]John Edison Arevalo Ovalle, Fabio A. González, Raúl Ramos-Pollán, José Luís Oliveira, Miguel Ángel Guevara-López:
Representation learning for mammography mass lesion classification with convolutional neural networks. Comput. Methods Programs Biomed. 127: 248-257 (2016) - [c12]Dario Garcia-Gasulla, Jonathan Moreno, Raúl Ramos-Pollán, Romel Casadiegos Barrios, Javier Béjar, Ulises Cortés, Eduard Ayguadé, Jesús Labarta, Toyotaro Suzumura:
On the Representativeness of Convolutional Neural Networks Layers. CCIA 2016: 29-38 - [c11]Susana Sanchez-Naranjo, Fabio A. González, Raúl Ramos-Pollán, Marc Solé:
Data driven Vertical Total Electron Content workflow for GNSS positioning for single frequency receivers. ICL-GNSS 2016: 1-6 - 2015
- [c10]Sebastián Sierra, Juan F. Molina, Angel Cruz-Roa, José Daniel Pabón, Raúl Ramos-Pollán, Fabio A. González, Hugo Franco:
Classification of Low-Level Atmospheric Structures Based on a Pyramid Representation and a Machine Learning Method. CIARP 2015: 19-26 - [c9]Francy Camacho, Rodrigo Torres, Raúl Ramos-Pollán:
Feature Learning Using Stacked Autoencoders to Predict the Activity of Antimicrobial Peptides. CMSB 2015: 121-132 - [c8]John Edison Arevalo Ovalle, Fabio A. González, Raúl Ramos-Pollán, José Luís Oliveira, Miguel Ángel Guevara-López:
Convolutional neural networks for mammography mass lesion classification. EMBC 2015: 797-800 - [c7]Diego Rueda-Plata, Raúl Ramos-Pollán, Fabio A. González:
Supervised Greedy Layer-Wise Training for Deep Convolutional Networks with Small Datasets. ICCCI (1) 2015: 275-284 - 2014
- [c6]John Edison Arevalo Ovalle, Raúl Ramos-Pollán, Fabio A. González:
Distributed Cache Strategies for Machine Learning Classification Tasks over Cluster Computing Resources. CARLA 2014: 43-53 - 2013
- [c5]Daniel Cardoso Moura, Miguel Ángel Guevara-López, Pedro Cunha, Naimy González-de-Posada, Raúl Ramos-Pollán, Isabel Ramos, Joana Pinheiro Loureiro, Inês C. Moreira, Bruno M. Ferreira de Araújo, Teresa Cardoso Fernandes:
Benchmarking Datasets for Breast Cancer Computer-Aided Diagnosis (CADx). CIARP (1) 2013: 326-333 - 2012
- [j3]Damià Segrelles, Ignacio Blanquer, José Salavert Torres, Vicente Hernández, Jose Miguel Franco-Valiente, Guillermo Díaz-Herrero, Raúl Ramos-Pollán, Rosana Medina, Luis Martí-Bonmatí, Miguel Ángel Guevara-López, Naymi González, Joana Loureiro, Isabel Ramos:
Exchanging Data for Breast Cancer Diagnosis on Heterogeneous Grid Platforms. Comput. Informatics 31(1): 3-15 (2012) - [j2]Raúl Ramos-Pollán, Miguel Ángel Guevara-López, Eugénio C. Oliveira:
A Software Framework for Building Biomedical Machine Learning Classifiers through Grid Computing Resources. J. Medical Syst. 36(4): 2245-2257 (2012) - [j1]Raúl Ramos-Pollán, Miguel Ángel Guevara-López, Cesar Suarez Ortega, Guillermo Díaz-Herrero, Jose Miguel Franco-Valiente, Manuel Rubio del Solar, Naimy González-de-Posada, Mário Augusto Pires Vaz, Joana Loureiro, Isabel Ramos:
Discovering Mammography-based Machine Learning Classifiers for Breast Cancer Diagnosis. J. Medical Syst. 36(4): 2259-2269 (2012) - [c4]Jorge A. Vanegas, Juan C. Caicedo, Jorge E. Camargo, Raúl Ramos-Pollán, Fabio A. González:
Bioingenium at ImageCLEF 2012: Text and Visual Indexing for Medical Images. CLEF (Online Working Notes/Labs/Workshop) 2012 - [c3]Raúl Ramos-Pollán, Fabio A. González, Juan C. Caicedo, Angel Cruz-Roa, Jorge E. Camargo, Jorge A. Vanegas, Santiago A. Perez, José David Bermeo, Juan Sebastian Otálora Montenegro, Paola K. Rozo, John Edison Arevalo Ovalle:
BIGS: A framework for large-scale image processing and analysis over distributed and heterogeneous computing resources. eScience 2012: 1-8 - 2010
- [c2]Raúl Ramos-Pollán, Miguel Ángel Guevara-López, Eugénio C. Oliveira:
Introducing ROC Curves as Error Measure Functions: A New Approach to Train ANN-Based Biomedical Data Classifiers. CIARP 2010: 517-524
2000 – 2009
- 2009
- [c1]Marcelo R. Risk, Francisco Prieto Castrillo, Juan Francisco Garcia Eijo, Cesar Suarez Ortega, María Botón-Fernández, Alfonso Pardo Diaz, Manuel Rubio del Solar, Raúl Ramos-Pollán:
CardioGRID: a framework for the analysis of cardiological signals in GRID computing. LANOMS 2009
Coauthor Index
aka: Fabio Augusto González Osorio
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last updated on 2024-11-08 20:32 CET by the dblp team
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