Search Results for author:
Found 19 papers, 11 papers with code
Sleep Activity Recognition and Characterization from Multi-Source Passively Sensed Data
Sleep constitutes a key indicator of human health, performance, and quality of life.
Heterogeneous Hidden Markov Models for Sleep Activity Recognition from Multi-Source Passively Sensed Data
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.
PyHHMM: A Python Library for Heterogeneous Hidden Markov Models
We introduce PyHHMM, an object-oriented open-source Python implementation of Heterogeneous-Hidden Markov Models (HHMMs).
Modular Gaussian Processes for Transfer Learning
We present a framework for transfer learning based on modular variational Gaussian processes (GP).
Regularizing Transformers With Deep Probabilistic Layers
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.
Deep Autoregressive Models with Spectral Attention
In this paper, we propose a forecasting architecture that combines deep autoregressive models with a Spectral Attention (SA) module, which merges global and local frequency domain information in the model's embedded space.
Medical data wrangling with sequential variational autoencoders
Medical data sets are usually corrupted by noise and missing data.
Unsupervised Learning of Global Factors in Deep Generative Models
We present a novel deep generative model based on non i. i. d.
Passive detection of behavioral shifts for suicide attempt prevention
More than one million people commit suicide every year worldwide.
Recyclable Gaussian Processes
We present a new framework for recycling independent variational approximations to Gaussian processes.
Multinomial Sampling for Hierarchical Change-Point Detection
Bayesian change-point detection, together with latent variable models, allows to perform segmentation over high-dimensional time-series.
Robust Sampling in Deep Learning
Deep learning requires regularization mechanisms to reduce overfitting and improve generalization.
Deep Sequential Models for Suicidal Ideation from Multiple Source Data
This article presents a novel method for predicting suicidal ideation from Electronic Health Records (EHR) and Ecological Momentary Assessment (EMA) data using deep sequential models.
Continual Multi-task Gaussian Processes
We then demonstrate that it is possible to derive GP models over many types of sequential observations, either discrete or continuous and amenable to stochastic optimization.
Continual Learning for Infinite Hierarchical Change-Point Detection
Change-point detection (CPD) aims to locate abrupt transitions in the generative model of a sequence of observations.
Change-Point Detection on Hierarchical Circadian Models
This paper addresses the problem of change-point detection on sequences of high-dimensional and heterogeneous observations, which also possess a periodic temporal structure.
Heterogeneous Multi-output Gaussian Process Prediction
We present a novel extension of multi-output Gaussian processes for handling heterogeneous outputs.
Individual performance calibration using physiological stress signals
The relation between performance and stress is described by the Yerkes-Dodson Law but varies significantly between individuals.
Bayesian Nonparametric Crowdsourcing
Crowdsourcing has been proven to be an effective and efficient tool to annotate large datasets.
