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Fast inference of latent space dynamics in huge relational event networks
Bayesian and EM-type algorithms have been proposed for inferring the latent space, but both the sheer size many social network applications as well as the dynamic nature of the process, and therefore the latent space, make computations prohibitively expensive.
Reproducing kernel Hilbert space based estimation of systems of ordinary differential equations
Non-linear systems of differential equations have attracted the interest in fields like system biology, ecology or biochemistry, due to their flexibility and their ability to describe dynamical systems.
High dimensional Sparse Gaussian Graphical Mixture Model
This paper considers the problem of networks reconstruction from heterogeneous data using a Gaussian Graphical Mixture Model (GGMM).
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