Pablo Navarrete Michelini

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We provide an overview of models of vehicular motion that are based on continuous-time Markov chains. Of these models, we concentrate on the subset represented by correlated random walks because they are general enough to capture essential patterns of the mobility of vehicles and simple enough to allow the analytical study of special but still realistic(More)
We interpret convolutional networks as adaptive filters and combine them with so–called MuxOut layers to efficiently upscale low resolution images. We formalize this interpretation by deriving a linear and space–variant structure of a convolutional network when its activations are fixed. We introduce general purpose algorithms to analyze a network and show(More)
Multi-level numerical methods that obtain the exact solution of a linear system are presented. The methods are devised by combining ideas from the full multi-grid algorithm and perfect reconstruction filters. The problem is stated as whether a direct solver is possible in a full multi-grid scheme by avoiding smoothing iterations and using different coarse(More)
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