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Footprint removal from seismic data with residual dictionary learning
(Society of Exploration Geophysicists, 2020-03)
Dictionary learning (DL) is a machine learning technique that can be used to find a sparse representation of a given data set by means of a relatively small set of atoms, which are learned from the input data. DL allows ...
Learning from Incomplete Features by Simultaneous Training of Neural Networks and Sparse Coding
(IEEE, 2021)
In this paper, the problem of training a classifier on a dataset with incomplete features is addressed. We assume that different subsets of features (random or structured) are available at each data instance. This situation ...
Virtual collaborative environments for individual learningAmbientes colaborativos virtuales para el aprendizaje individual
(Universidad de Costa Rica, 2017)
Automatic quantification of the LV function and mass: A deep learning approach for cardiovascular MRI
(Elsevier, 2019-02)
Objective: This paper proposes a novel approach for automatic left ventricle (LV) quantification using convolutional neural networks (CNN). Methods: The general framework consists of one CNN for detecting the LV, and another ...
A context-aware approach to automated negotiation using reinforcement learning
(Elsevier, 2021-01)
Agents negotiate depending on individual perceptions of facts, events, trends and special circumstances that define the negotiation context. The negotiation context affects in different ways each agent's preferences, ...
Sleep and its interferent in learningSONO E SEUS INTERFERENTES NA APRENDIZAGEM
(Faculdade de Filosofia e Ciências, 2018)
Cooperative learning in the teaching of mathematicsEl aprendizaje cooperativo en la enseñanza de la matematica
(Universidad del Zulia, 2021)
Mining Early Life Risk and Resiliency Factors and Their Influences in Human Populations from PubMed: A Machine Learning Approach to Discover DOHaD Evidence
(Multidisciplinary Digital Publishing Institute, 2021-10-22)
The Developmental Origins of Health and Disease (DOHaD) framework aims to understand how early life exposures shape lifecycle health. To date, no comprehensive list of these exposures and their interactions has been ...