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This repository contains the code used to produce the results presented in the IJCNN 2017 paper "DropIn: Making Reservoir Computing Neural Networks Robust to Missing Inputs by Dropout" by D. Bacciu, F. Crecchi (University of Pisa) and D. Morelli (Biobeats LTD).
d-stem-LUR: Companion repository for the paper entitled "Concurrent spatiotemporal daily land use regression modeling and missing data imputation of fine particulate matter using distributed space-time expectation maximization", GitHub, 2019. The paper is published in the Atmospheric Environment journal
Trabajo de Fin de Grado, dedicado al problema "Viajante del Comercio"/"Travelling Salesman Problem". Dicho trabajo consta de un análisis del problema, aplicación de una metodología propia de investigación y el diseño de un algoritmo que ofrezca resultados satisfactorios al problema.