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Reinforcement learning algorithms have been successfully applied in robotics to learn how to solve tasks based on reward signals obtained during task execution. These reward signals are usually modeled by the programmer or provided by supervision. However, ...
The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maximum a posteriori sparse solutions and neglect to represent posterior uncertain ...
We present a method for the sparse greedy approximation of Bayesian Gaussian process regression, featuring a novel heuristic for very fast forward selection. Our method is essentially as fast as an equivalent one which selects the "support" patterns at ran ...
PyBrain is a versatile machine learning library for Python. Its goal is to provide flexible, easy-to-use yet still powerful algorithms for machine learning tasks, including a variety of predefined environments and benchmarks to test and compare algorithms. ...
In [8] Yamauchi and Beer explored the abilities of continuous time recurrent neural networks (CTRNNs) to display reinforcement-learning like abilities. The investigated tasks were generation and learning of short bit sequences. This "learning'' came about ...
The medical image registration problem can be formulated as a nonlinear programming problem. To identify appropriate algorithms to solve it, it is critical to analyze the properties of the problem. In this paper, we consider the image registration problem ...
This paper proposes an approach allowing topology learning and recognition in indoor environments by using a probabilistic approach called Bayesian Programming. The main goal of this approach is to cope with the uncertainty, imprecision and incompleteness ...
One of the difficulties of extracting text contained in images or videos comes from the variation of the grayscale values of the text and backgrounds. In this paper we propose a new method to normalize the contrast between text characters and backgrounds s ...
In this paper, we propose a scalable algorithm for spectral embedding. The latter is a standard tool for graph clustering. However, its computational bottleneck is the eigendecomposition of the graph Laplacian matrix, which prevents its application to larg ...
With ever-increasing power densities, Dynamic Thermal Management (DTM) techniques have become mainstream in today’s systems. An important component of such techniques is the thermal trigger. It has been shown that predictive thermal triggers can outperform ...