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Fine-Tuning Dropout Regularization in Energy-Based Deep Learning
(2021-01-01)
Deep Learning architectures have been extensively studied in the last years, mainly due to their discriminative power in Computer Vision. However, one problem related to such models concerns their number of parameters and ...
Towards vegetation species discrimination by using data-driven descriptors
(Ieee, 2016-01-01)
In this paper, we analyse the use of Convolutional Neural Networks (CNNs or ConvNets) to discriminate vegetation species with few labelled samples. To the best of our knowledge, this is the first work dedicated to the ...
Towards vegetation species discrimination by using data-driven descriptors
(2017-02-28)
In this paper, we analyse the use of Convolutional Neural Networks (CNNs or ConvNets) to discriminate vegetation species with few labelled samples. To the best of our knowledge, this is the first work dedicated to the ...
Image reconstruction from projections of digital breast tomosynthesis using deep learning
(2021-01-01)
The Filtered Backprojection (FBP) algorithm for Computed Tomography (CT) reconstruction can be mapped entire in an Artificial Neural Network (ANN), with the backprojection (BP) operation simulated analytically in a layer ...
Boosted projections and low cost transfer learning applied to smart surveillance
(Universidade Federal de Minas GeraisUFMG, 2018-02-23)
Computer vision is an important area related to understanding the world through images. It can be used in biometrics, by verifying whether a given face is of a certain identity, used to look for crime perpetrators in an ...
A Novel Adaptive Deep Learning Model of Covid-19 with focus on mortality reduction strategies
We employ deep learning to propose an Artificial Neural Network (ANN) based and data stream guided real-time incremental
learning algorithm for parameter estimation of a non-intrusive, intelligent, adaptive and online ...
A metaheuristic-driven approach to fine-tune Deep Boltzmann Machines
(Elsevier B.V., 2020-12-01)
Deep learning techniques, such as Deep Boltzmann Machines (DBMs), have received considerable attention over the past years due to the outstanding results concerning a variable range of domains. One of the main shortcomings ...
Espectroscopia con infrarrojo y técnicas de Machine learning y Deep learning para la detección y clasificación de frutas para la agroindustria. Caso: arándanos - Empresa Talsa - 2018
(Universidad Privada Antenor Orrego - UPAO, 2019)
ESPECTROSCOPIA CON INFRARROJO Y TECNICAS DE MACHINE LEARNING Y DEEP LEARNING PARA LA DETECCIÓN Y CLASIFICACIÓN DE FRUTAS PARA LA AGROINDUSTRIA. CASO: ARÁNDANOS - EMPRESA TalSA -2018
Las empresas comercializadoras de frutas, ...