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Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are t...

Recurrent neural networks (RNNs) have been widely adopted in research areas concerned with sequential data, such as text, audio, and video. However, RNNs consisting of sigma cells or tanh cells are unable to learn the re...

Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting. Overfitting refers to the phenomenon when a n...

The popular Q-learning algorithm is known to overestimate action values under certain conditions. It was not previously known whether, in practice, such overestimations are common, whether they harm performance, and whet...

Importance: Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules ...

Due to the simplicity of their implementations, least square support vector machine (LS-SVM) and proximal support vector machine (PSVM) have been widely used in binary classification applications. The conventional LS-SVM...

Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning alg...

Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying dis...

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...

In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in ...

"This thesis investigates the application of machine learning methods to the task of deforestation monitoring using time-series satellite data. The objective is to assess how different algorithmic approaches perform in d...

Features such as fast response, storage efficiency, fault tolerance and graceful degradation in face of scarce or spurious inputs make neural networks appropriate tools for Intelligent Computer Systems. A neural network ...

In 21st century, convergences of technologies and services in heterogeneous environment have contributed multi-traffic. This scenario will affect computer network on learning system in higher educational Institutes. Impl...

Virtual learning environments are widely used in distance education as the learning space of choice, mainly for interaction and collaboration. However, they generally don't provide means to integrate easily other learnin...

Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a ...

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 ...

Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of gr...

Institutional collaborative networks are a central phenomenon in improving democratic governance. This study aims to design and validate an evaluation grid based on the perceptions of actors who participate in such netwo...

Los cambios que afectan a las instituciones educativas universitarias ocurren en distintos órdenes y se refieren a: cambios en el conocimiento (en la generación, gestión y distribución del mismo); cambios socio-cultu...

El concepto de objeto de aprendizaje se ha convertido en el centro de un nuevo paradigma de diseño de actividades de aprendizaje, poniendo énfasis en la reutilización de contenidos y actividades. Surge por consiguient...

Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progres...

In the last decade, there has been a significant increase in the number of papers related to machine learning and the application of machine learning in various fields of science. Belmonte et al. observed that between 20...

Abstract The crack fault is one of the most common faults in the rotor system, and researchers have paid close attention to its fault diagnosis. However, most studies focus on discussing the dynamic response characterist...

The deep-learning revolution is providing enticing new opportunities to manipulate and harness light at all scales. By building models of light–matter interactions from large experimental or simulated datasets, deep le...