Representative datasets for neural networks

Neural networks present big popularity and success in many fields. The large training time process problem is a very important task nowadays. In this paper, a new approach to get over this issue based on reducing dataset size is proposed. Two algorithms covering two different shape notions are shown and experimental results are given.Ministerio de Economía y Competitividad MTM2015-67072-

Towards a Philological Metric through a Topological Data Analysis Approach

The canon of the baroque Spanish literature has been thoroughly studied with philological techniques. The major representatives of the poetry of this epoch are Francisco de Quevedo and Luis de Góngora y Argote. They are commonly classified by the literary experts in two different streams: Quevedo belongs to the Conceptismo and Góngora to the Culteranismo. Besides, traditionally, even if Quevedo is considered the most representative of the Conceptismo, Lope de Vega is also considered to be, at least, closely related to this literary trend. In this paper, we use Topological Data Analysis techniques to provide a first approach to a metric distance between the literary style of these poets. As a consequence, we reach results that are under the literary experts’ criteria, locating the literary style of Lope de Vega, closer to the one of Quevedo than to the one of Góngora

Representative Datasets: The Perceptron Case

One of the main drawbacks of the practical use of neural networks is the long time needed in the training process. Such training process consists in an iterative change of parameters trying to minimize a loss function. These changes are driven by a dataset, which can be seen as a set of labeled points in an n-dimensional space. In this paper, we explore the concept of representative dataset which is smaller than the original dataset and satisfies a nearness condition independent of isometric transformations. The representativeness is measured using persistence diagrams due to its computational efficiency. We also prove that the accuracy of the learning process of a neural network on a representative dataset is comparable with the accuracy on the original dataset when the neural network architecture is a perceptron and the loss function is the mean squared error. These theoretical results accompanied with experimentation open a door to the size reduction of the dataset in order to gain time in the training process of any neural network

Topology-based representative datasets to reduce neural network training resources

One of the main drawbacks of the practical use of neural networks is the long time required in the training process. Such a training process consists of an iterative change of parameters trying to minimize a loss function. These changes are driven by a dataset, which can be seen as a set of labeled points in an n-dimensional space. In this paper, we explore the concept of a representative dataset which is a dataset smaller than the original one, satisfying a nearness condition independent of isometric transformations. Representativeness is measured using persistence diagrams (a computational topology tool) due to its computational efficiency. We theoretically prove that the accuracy of a perceptron evaluated on the original dataset coincides with the accuracy of the neural network evaluated on the representative dataset when the neural network architecture is a perceptron, the loss function is the mean squared error, and certain conditions on the representativeness of the dataset are imposed. These theoretical results accompanied by experimentation open a door to reducing the size of the dataset to gain time in the training process of any neural networkAgencia Estatal de Investigación PID2019-107339GB-100Agencia Andaluza del Conocimiento P20-0114

Beyond sustainability: for an Environmental Education that increases the resilience of the population in the face of degrowth/collapse

En el presente artículo abordamos la problemática asociada al uso del concepto de sostenibilidad en Educación Ambiental. Se presenta una crítica razonada en relación con el contexto actual de decrecimiento/colapso. Apostamos por una educación en y para el decrecimiento como alternativa a la educación para la sostenibilidad.In this paper we address the problems associated with the use of the concept of sustainability in Environmental Education. A reasoned critique is presented in relation to the current context of degrowth/collapse. We bet for an education in and for degrowth as an alternative to education for sustainability

Optimizing the Simplicial-Map Neural Network Architecture

Simplicial-map neural networks are a recent neural network architecture induced by simplicial maps defined between simplicial complexes. It has been proved that simplicial-map neural networks are universal approximators and that they can be refined to be robust to adversarial attacks. In this paper, the refinement toward robustness is optimized by reducing the number of simplices (i.e., nodes) needed. We have shown experimentally that such a refined neural network is equivalent to the original network as a classification tool but requires much less storage.Agencia Estatal de Investigación PID2019-107339GB-10

Antecedentes del uso de los medios sociales por el turista: motivación, oportunidad y capacidad

This work uses the MOA model to explain to what level motivation, opportunity and ability of users are factors determining the intentions to use social media when organizing and taking vacation trips. The conclusions of the study reveal that the intentions to use social media are directly influenced by the motivation and ability of users; however, the opportunity does not significantly affect the predisposition to use such technologies. In turn, functional and hedonic benefits have an influence on motivations, while social benefits do not.El presente trabajo utiliza el modelo MOA para analizar en qué medida la motivación, la oportunidad y la capacidad de los usuarios son factores determinantes de las intenciones de uso de medios sociales en la organización y desarrollo de viajes turísticos. Las conclusiones del estudio revelan que las intenciones de uso de los medios sociales se ven afectadas por la motivación y las capacidades de los usuarios y, sin embargo, no se ven influenciadas por la oportunidad. A su vez, en las motivaciones influyen los beneficios funcionales y hedónicos, pero no los sociales

Menos es más (complejidad) : una reflexión sobre la concepción de complejidad predominante en el pensamiento decrecentista

asociado a los límites biofísicos es frecuente encontrar la idea de que el decrecimiento supone una descomplejización del sistema social. Creemos que esta idea se fundamenta en una concepción del concepto de complejidad asociada a variables cuantitativas y a la idea de que lo "primitivo" y "menos complejo" incrementa la resiliencia de la población. Proponemos utilizar la noción de complejidad sustentada en la obra de Edgar Morin, concepción que nos ayuda a entender que el decrecimiento no supone, inevitablemente, un decremento de la complejidad del sistema social