Zobrazeno 1 - 10
of 263
pro vyhledávání: '"Artés-Rodríguez Antonio"'
Sleep constitutes a key indicator of human health, performance, and quality of life. Sleep deprivation has long been related to the onset, development, and worsening of several mental and metabolic disorders, constituting an essential marker for prev
Externí odkaz:
http://arxiv.org/abs/2301.10156
Psychiatric patients' passive activity monitoring is crucial to detect behavioural shifts in real-time, comprising a tool that helps clinicians supervise patients' evolution over time and enhance the associated treatments' outcomes. Frequently, sleep
Externí odkaz:
http://arxiv.org/abs/2211.10371
We introduce PyHHMM, an object-oriented open-source Python implementation of Heterogeneous-Hidden Markov Models (HHMMs). In addition to HMM's basic core functionalities, such as different initialization algorithms and classical observations models, i
Externí odkaz:
http://arxiv.org/abs/2201.06968
We present a framework for transfer learning based on modular variational Gaussian processes (GP). We develop a module-based method that having a dictionary of well fitted GPs, one could build ensemble GP models without revisiting any data. Each mode
Externí odkaz:
http://arxiv.org/abs/2110.13515
Language models (LM) have grown with non-stop in the last decade, from sequence-to-sequence architectures to the state-of-the-art and utter attention-based Transformers. In this work, we demonstrate how the inclusion of deep generative models within
Externí odkaz:
http://arxiv.org/abs/2108.10764
Time series forecasting is an important problem across many domains, playing a crucial role in multiple real-world applications. In this paper, we propose a forecasting architecture that combines deep autoregressive models with a Spectral Attention (
Externí odkaz:
http://arxiv.org/abs/2107.05984
Medical data sets are usually corrupted by noise and missing data. These missing patterns are commonly assumed to be completely random, but in medical scenarios, the reality is that these patterns occur in bursts due to sensors that are off for some
Externí odkaz:
http://arxiv.org/abs/2103.07206
We present a novel deep generative model based on non i.i.d. variational autoencoders that captures global dependencies among observations in a fully unsupervised fashion. In contrast to the recent semi-supervised alternatives for global modeling in
Externí odkaz:
http://arxiv.org/abs/2012.08234
Autor:
Moreno-Muñoz, Pablo, Romero-Medrano, Lorena, Moreno, Ángela, Herrera-López, Jesús, Baca-García, Enrique, Artés-Rodríguez, Antonio
More than one million people commit suicide every year worldwide. The costs of daily cares, social stigma and treatment issues are still hard barriers to overcome in mental health. Most symptoms of mental disorders are related to the behavioral state
Externí odkaz:
http://arxiv.org/abs/2011.09848
We present a new framework for recycling independent variational approximations to Gaussian processes. The main contribution is the construction of variational ensembles given a dictionary of fitted Gaussian processes without revisiting any subset of
Externí odkaz:
http://arxiv.org/abs/2010.02554