13 accepted papers
Expert-annotated time series datasets often suffer from low agreement, especially in medical applications where decisions rely on subjective criteria and inconsistent thresholds. Such variability degrades annotation quality and thus limits the reliability of supervised classification models. To addr…
Finding patterns in time series is crucial to understanding physical or physiological phenomena monitored with sensors. Convolutional sparse coding (CSC) methods, which approximate signals by a sparse combination of short signal templates (also called atoms), are well-suited for this task. Neverthel…
We propose a new approach for covariance change point detection applied to graph signals. Specifically, our method draws on the notion of graph stationarity to derive a relevant parameterization of the covariance matrix that can be used in a cost function. This parameterization allows prior graph kn…
Human physiological signals tend to exhibit both global and local structures: the former are shared across a population, while the latter reflect inter-individual variability. For instance, kinetic measurements of the gait cycle during locomotion present common characteristics, although idiosyncras…
Automatically extracting robust representations from large and complex time series data is becoming imperative for several real-world applications. Unfortunately, the potential of common neural network architectures in capturing invariant properties of time series remains relatively underexplored. F…
In this work, we leverage recent graph uncertainty principles to introduce a new dictionary learning method on graphs. Our method considers two distinct classes of atoms, spatially local atoms on the graph, and smooth atoms, together with well suited penalty functions. Notably, the consideration of…
Analyzing inter-individual variability of physiological functions is particularly appealing in medical and biological contexts to describe or quantify health conditions. Such analysis can be done by comparing individuals to a reference one with time series as biomedical data. This paper introduces a…
The introduction of affordable wearable cameras and eye trackers have led to a massive amount of egocentric (or first-person view) videos, bringing new challenges to the computer vision community for understanding and leveraging the specificities of the egocentric view. This work proposes a novel ap…
In this paper, we propose an adaptive subsampling method for multidomain signals based on the constrained learning of a product graph. Given an input multidomain signal, we search for a product graph on which the signal is bandlimited, i.e. have limited spectral occupancy. The subsampling procedure…
In this article, we propose to extend the classical Convolutional Sparse Coding model (CSC) to multivariate data by introducing a new tensor CSC model that enforces sparsity and low-rank constraint on the activations. The advantages of this model are threefold. First, by using tensor algebra, this m…
In this paper, we present a method for learning an underlying graph topology using observed graph signals as training data. The novelty of our method lies on the combination of two assumptions that are imposed as constraints to the graph learning process: i) the standard assumption used in the liter…
In this article, we propose an automatic procedure to calibrate change point detection algorithms. Our approach expands on the ability of an expert to provide very rough segmentation estimates, called partial annotations, for a few signal examples. Our contribution consists in a supervised strategy…
In this paper, we introduce DICOD, a convolutional sparse coding algorithm which builds shift invariant representations for long signals. This algorithm is designed to run in a distributed setting, with local message passing, making it communication efficient. It is based on coordinate descent and u…