Recent progress in machine learning techniques have revived interest in building artificial general intelligence using these particular tools. There …

For systems with hidden attractors and unstable equilibria, the property that hidden attractors are not connected with unstable equilibria is now acc…

In this paper, we present the first study that compares different models of Bayesian Neural Networks (BNNs) to predict the posterior distribution of …

Learning an embedding for a large collection of items is a popular approach to overcome the computational limitations associated to one-hot encodings…

In the present paper we have discussed in some detail the Wasserstein geometric properties of the Gaussian densities manifold. We have followed a known argument based on the geometric notion of submersion. We have improved upon what is known in the …

The Wasserstein distance on multivariate non-degenerate Gaussian densities is a Riemannian distance. After reviewing the properties of the distance a…

The objective of this paper is to prove the convergence of a linear implicit multi-step numerical method for ordinary differential equations. The alg…

In this paper we propose two novel bounds for the log-likelihood based on Kullback-Leibler and the R\'{e}nyi divergences, which can be used for varia…

Convolutional Neural Networks (CNNs) are build specifically for computer vision tasks for which it is known that the input data is a hierarchical str…

Dynamical systems, whether continuous or discrete, are used by physicists in order to study non-linear phenomena. In the case of discrete dynamical s…

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