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Security governance surrounding infectious diseases necessitates collaboration and information sharing among diverse actors, including nation-states and institutions. The Global Antimicrobial Resistance Surveillance Syst...

La inteligencia artificial permite la innovación y el cambio en todos los aspectos de la vida moderna. La mayoría de los avances actuales se basan en Deep Learning, un área de conocimiento muy madura que permite a las...

The network society (Van Dijk, 2020) has completely blurred the traditional difference between virtual and real life, Our day to day is increasingly characterised by our continuous connection to the Internet, Multiple da...

The Artificial Intelligence (AI) revolution, in its different manifestations -expert systems, neural networks, intelligent hybrid systems, etc.- brings with it many technological advances. Although this technology has so...

El vertiginoso crecimiento y la precisión de las técnicas de Inteligencia Artificial (AI, del inglés Artificial Intelligence) permiten analizar grandes volúmenes de datos de forma rápida y eficiente. En ese sentido,...

Subjects / keywords: Business intelligence; Computer network resources; Industrial management

Subjects / keywords: ChatGPT; Neural networks (Computer science); Artificial intelligence; Chatbots; Social networks

Subjects / keywords: Artificial intelligence; Neural networks (Computer science); Art and technology

Subjects / keywords: Intelligence service; Computer networks; Colombia

Subjects / keywords: Neural networks (Computer science); Artificial intelligence; Electronic data processing; Electronic books

Subjects / keywords: Neural networks (Computer science); Artificial intelligence; Information networks; Machine learning

Subjects / keywords: Artificial intelligence; Neural networks (Computer science); Electronic books

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, improve on the previous best result in semant...

We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural...

Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224 × 224) input image. This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrar...

A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is presented. Multiscale image features are combined into a single topographical saliency map. A dynam...

In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with...

The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most in...

State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] have reduced the running time of these detection networks, expo...

The central building block of convolutional neural networks (CNNs) is the convolution operator, which enables networks to construct informative features by fusing both spatial and channel-wise information within local re...

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors ...