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The Artificial Neural Networks (ANN) training represents a time-consuming process in machine learning systems. In this work we provide an implementation of the back-propagation algorithm on CUDA, a parallel computing architecture developed by NVIDIA. Using CUBLAS, a CUDA implementation of the Basic Linear Algebra Subprograms library (BLAS), the process is(More)
We introduce a supervised reinforcement learning (SRL) architecture for robot control problems with high dimensional state spaces. Based on such architecture two new SRL algorithms are proposed. In our algorithms, a behavior model learned from examples is used to dynamically reduce the set of actions available from each state during the early reinforcement(More)
Using multilayer perceptrons (MLPs) to approximate the state-action value function in reinforcement learning (RL) algorithms could become a nightmare due to the constant possibility of unlearning past experiences. Moreover, since the target values in the training examples are bootstraps values, this is, estimates of other estimates, the chances to get stuck(More)
Chagas disease is a tropical parasitic disease caused by the flagellate protozoan Trypanosoma cruzi (T. cruzi) and currently affecting large portions of the Americas. One of the standard laboratory methods to determine the presence of the parasite is by direct visualization in blood smears stained with some colorant. This method is time-consuming, requires(More)
Quantification of impact craters on planetary surfaces is relevant to understand the geological history of the planet. In order to automatize quantification of lunar craters in digital images, the first step is to develop a computational tool capable of classifying a subwindow of pixels into two possible outputs: crater / non-crater. In this paper, we(More)
We present preliminary results derived from our project on robot self localization using omni directional images. In our approach, features are generated through the computation of covariance matrices that capture important patterns that relates changes in pixel intensities. The learning models used are Mixture of Gaussians and Gaussian Discriminant(More)