23 citations · 40 across the 10 of their papers we have counts for
15 papers
Heterogeneous Hidden Markov Models for Sleep Activity Recognition from Multi-Source Passively Sensed Data
Fernando Moreno-Pino, María Martínez-García, Pablo M. Olmos +1
Psychiatric patients' passive activity monitoring is crucial to detect behavioural shifts in real-time, comprising a tool that helps clinicians supervise patients' evolution over t…
PyHHMM: A Python Library for Heterogeneous Hidden Markov Models
Fernando Moreno-Pino, Emese Sükei, Pablo M. Olmos +1
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 di…
Modular Gaussian Processes for Transfer Learning
Pablo Moreno-Muñoz, Antonio Artés-Rodríguez, Mauricio A. Álvarez
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…
Regularizing Transformers With Deep Probabilistic Layers
Aurora Cobo Aguilera, Pablo Martínez Olmos, Antonio Artés-Rodríguez +1
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 wo…
Medical data wrangling with sequential variational autoencoders
Daniel Barrejón, Pablo M. Olmos, Antonio Artés-Rodríguez
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 tha…
Unsupervised Learning of Global Factors in Deep Generative Models
Ignacio Peis, Pablo M. Olmos, Antonio Artés-Rodríguez
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 contr…