Deep Residual Learning for Image Recognition
Actualmente diversas investigaciones se han enfocado en analizar a partir de videos de alta velocidad, características de las descargas eléctricas atmosféricas con el fin de adq...
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3,290 publications
Long Short-Term Memory
Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We brief...
ImageNet classification with deep convolutional neural networks
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On t...
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement
se ha posicionado como un centro de referencia asistencial y académica, con la proyección de conformar clínicas o centros de excelencia.Para lograr este objetivo, es indispensab...
The CES-D Scale
The CES-D scale is a short self-report scale designed to measure depressive symptomatology in the general population. The items of the scale are symptoms associated with depress...
Going deeper with convolutions
We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Sca...
Prospect Theory: An Analysis of Decision under Risk
This paper presents a critique of expected utility theory as a descriptive model of decision making under risk, and develops an alternative model, called prospect theory. Choice...
Densely Connected Convolutional Networks
Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to ...
Regression Models and Life-Tables
Summary The analysis of censored failure times is considered. It is assumed that on each individual are available values of one or more explanatory variables. The hazard functio...
Adaptation in Natural and Artificial Systems
Genetic algorithms are playing an increasingly important role in studies of complex adaptive systems, ranging from adaptive agents in economic theory to the use of machine learn...
Fully convolutional networks for semantic segmentation
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, ex...