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@article{DBLP:journals/corr/abs-2410-07840,

title = {Protect Before Generate: Error Correcting Codes within Discrete Deep  Generative Models},

author = {Mar'ıa Mart'ınez-Garc'ıa and Grace Villacrés and David G. M. Mitchell and Pablo M. Olmos},

url = {https://doi.org/10.48550/arXiv.2410.07840},

doi = {10.48550/ARXIV.2410.07840},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {CoRR},

volume = {abs/2410.07840},

abstract = {Despite advances in deep probabilistic models, learning discrete latent representations remains challenging. This work introduces a novel method to improve inference in discrete Variational Autoencoders by reframing the inference problem through a generative perspective. We conceptualize the model as a communication system, and propose to leverage Error-Correcting Codes (ECCs) to introduce redundancy in latent representations, allowing the variational posterior to produce more accurate estimates and reduce the variational gap. We present a proof-of-concept using a Discrete Variational Autoencoder with binary latent variables and low-complexity repetition codes, extending it to a hierarchical structure for disentangling global and local data features. Our approach significantly improves generation quality, data reconstruction, and uncertainty calibration, outperforming the uncoded models even when trained with tighter bounds such as the Importance Weighted Autoencoder objective. We also outline the properties that ECCs should possess to be effectively utilized for improved discrete variational inference.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/titb/MartinezGarciaO23,

title = {Handling Ill-Conditioned Omics Data With Deep Probabilistic Models},

author = {Mar'ıa Mart'ınez-Garc'ıa and Pablo M. Olmos},

url = {https://doi.org/10.1109/JBHI.2023.3279493},

doi = {10.1109/JBHI.2023.3279493},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {IEEE J. Biomed. Health Informatics},

volume = {27},

number = {9},

pages = {4601–4610},

abstract = {The advent of high-throughput technologies has produced an increase in the dimensionality of omics datasets, which limits the application of machine learning methods due to the great unbalance between the number of observations and features. In this scenario, dimensionality reduction is essential to extract the relevant information within these datasets and project it in a low-dimensional space, and probabilistic latent space models are becoming popular given their capability to capture the underlying structure of the data as well as the uncertainty in the information. This article aims to provide a general classification and dimensionality reduction method based on deep latent space models that tackles two of the main problems that arise in omics datasets: the presence of missing data and the limited number of observations against the number of features. We propose a semi-supervised Bayesian latent space model that infers a low-dimensional embedding driven by the target label: the Deep Bayesian Logistic Regression (DBLR) model. During inference, the model also learns a global vector of weights that allows it to make predictions given the low-dimensional embedding of the observations. Since this kind of dataset is prone to overfitting, we introduce an additional probabilistic regularization method based on the semi-supervised nature of the model. We compared the performance of the DBLR against several state-of-the-art methods for dimensionality reduction, both in synthetic and real datasets with different data types. The proposed model provides more informative low-dimensional representations, outperforms the baseline methods in classification, and can naturally handle missing entries.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2301-10156,

title = {Sleep Activity Recognition and Characterization from Multi-Source  Passively Sensed Data},

author = {Mar'ıa Mart'ınez-Garc'ıa and Fernando Moreno-Pino and Pablo M. Olmos and Antonio Artés-Rodr'ıguez},

url = {https://doi.org/10.48550/arXiv.2301.10156},

doi = {10.48550/ARXIV.2301.10156},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {CoRR},

volume = {abs/2301.10156},

abstract = {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 preventing, evaluating, and treating different health conditions. Sleep Activity Recognition methods can provide indicators to assess, monitor, and characterize subjects' sleep-wake cycles and detect behavioral changes. In this work, we propose a general method that continuously operates on passively sensed data from smartphones to characterize sleep and identify significant sleep episodes. Thanks to their ubiquity, these devices constitute an excellent alternative data source to profile subjects' biorhythms in a continuous, objective, and non-invasive manner, in contrast to traditional sleep assessment methods that usually rely on intrusive and subjective procedures. A Heterogeneous Hidden Markov Model is used to model a discrete latent variable process associated with the Sleep Activity Recognition task in a self-supervised way. We validate our results against sleep metrics reported by tested wearables, proving the effectiveness of the proposed approach and advocating its use to assess sleep without more reliable sources.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DBLP:journals/corr/abs-2211-10371,

title = {Heterogeneous Hidden Markov Models for Sleep Activity Recognition  from Multi-Source Passively Sensed Data},

author = {Fernando Moreno-Pino and Mar'ıa Mart'ınez-Garc'ıa and Pablo M. Olmos and Antonio Artés-Rodr'ıguez},

url = {https://doi.org/10.48550/arXiv.2211.10371},

doi = {10.48550/ARXIV.2211.10371},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {CoRR},

volume = {abs/2211.10371},

abstract = {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 disturbances and mental health deterioration are closely related, as mental health condition worsening regularly entails shifts in the patients' circadian rhythms. Therefore, Sleep Activity Recognition constitutes a behavioural marker to portray patients' activity cycles and to detect behavioural changes among them. Moreover, mobile passively sensed data captured from smartphones, thanks to these devices' ubiquity, constitute an excellent alternative to profile patients' biorhythm.

In this work, we aim to identify major sleep episodes based on passively sensed data. To do so, a Heterogeneous Hidden Markov Model is proposed to model a discrete latent variable process associated with the Sleep Activity Recognition task in a self-supervised way. We validate our results against sleep metrics reported by clinically tested wearables, proving the effectiveness of the proposed approach.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}