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Recent works on word representations mostly rely on predictive models. Distributed word representations (aka word embeddings) are trained to optimally predict the contexts in which the corresponding words tend to appear. Such models have succeeded in captu ...
The aim of this short note is twofold. First, we give a sketch of the proof of a recent result proved by the authors in the paper [7] concerning existence and uniqueness of renormalized solutions of continuity equations with unbounded damping coefficient. ...
This paper discusses theoretical aspects of the modeling of the sources of the EEG (i.e., the bioelectromagnetic inverse problem or source localization problem). Using theHelmholtz decomposition (HD) of the current density vector (CDV) of the primary curre ...
Source imaging maps back boundary measurements to underlying generators within the domain; e. g., retrieving the parameters of the generating dipoles from electrical potential measurements on the scalp such as in electroencephalography (EEG). Fitting such ...
We study the distributed inference task over regression and classification models where the likelihood function is strongly log-concave. We show that diffusion strategies allow the KL divergence between two likelihood functions to converge to zero at the r ...
In this paper we provide a complete analogy between the Cauchy-Lipschitz and the DiPerna-Lions theories for ODE's, by developing a local version of the DiPerna-Lions theory. More precisely, we prove the existence and uniqueness of a maximal regular flow fo ...
Recently, an information-theoretical decomposition of Kullback–Leibler divergence into uncertainty, reliability, and resolution was introduced. In this article, this decomposition is generalized to the case where the observation is uncertain. Along with a ...
Many speech technology systems rely on Gaussian Mixture Models (GMMs). The need for a comparison between two GMMs arises in applications such as speaker verification, model selection or parameter estimation. For this purpose, the Kullback-Leibler (KL) dive ...
Electroencephalography (EEG) is a key modality to monitor brain activity with high temporal resolution. EEG makes use of an array of electrodes to measure the electrical potential on the scalp. While most traditional EEG analyses have looked at EEG rhythms ...
Domain-invariant representations are key to addressing the domain shift problem where the training and test examples follow different distributions. Existing techniques that have attempted to match the distributions of the source and target domains typical ...
