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Found 25 papers, 7 papers with code
Asynchronous Graph Generators
We introduce the asynchronous graph generator (AGG), a novel graph neural network architecture for multi-channel time series which models observations as nodes on a dynamic graph and can thus perform data imputation by transductive node generation.
Greedy online change point detection
Standard online change point detection (CPD) methods tend to have large false discovery rates as their detections are sensitive to outliers.
Gaussian process deconvolution
Let us consider the deconvolution problem, that is, to recover a latent source $x(\cdot)$ from the observations $\mathbf{y} = [y_1,\ldots, y_N]$ of a convolution process $y = x\star h + \eta$, where $\eta$ is an additive noise, the observations in $\mathbf{y}$ might have missing parts with respect to $y$, and the filter $h$ could be unknown.
Computationally-efficient initialisation of GPs: The generalised variogram method
We present a computationally-efficient strategy to initialise the hyperparameters of a Gaussian process (GP) avoiding the computation of the likelihood function.
Nonstationary multi-output Gaussian processes via harmonizable spectral mixtures
Kernel design for Multi-output Gaussian Processes (MOGP) has received increased attention recently.
Studying the Interplay between Information Loss and Operation Loss in Representations for Classification
From this, our main result shows that a specific form of vanishing information loss (a weak notion of asymptotic informational sufficiency) implies a vanishing MPE loss (or asymptotic operational sufficiency) when considering a general family of lossy continuous representations.
Detection of blue whale vocalisations using a temporal-domain convolutional neural network
We present a framework for detecting blue whale vocalisations from acoustic submarine recordings.
Bayesian autoregressive spectral estimation
Autoregressive (AR) time series models are widely used in parametric spectral estimation (SE), where the power spectral density (PSD) of the time series is approximated by that of the \emph{best-fit} AR model, which is available in closed form.
Late reverberation suppression using U-nets
In real-world settings, speech signals are almost always affected by reverberation produced by the working environment; these corrupted signals need to be \emph{dereverberated} prior to performing, e. g., speech recognition, speech-to-text conversion, compression, or general audio enhancement.
A novel notion of barycenter for probability distributions based on optimal weak mass transport
We introduce weak barycenters of a family of probability distributions, based on the recently developed notion of optimal weak transport of mass by Gozlanet al. (2017) and Backhoff-Veraguas et al. (2020).
Bayesian Reconstruction of Fourier Pairs
Our aim is to address the lack of a principled treatment of data acquired indistinctly in the temporal and frequency domains in a way that is robust to missing or noisy observations, and that at the same time models uncertainty effectively.
Gaussian process imputation of multiple financial series
In Financial Signal Processing, multiple time series such as financial indicators, stock prices and exchange rates are strongly coupled due to their dependence on the latent state of the market and therefore they are required to be jointly analysed.
MOGPTK: The Multi-Output Gaussian Process Toolkit
We present MOGPTK, a Python package for multi-channel data modelling using Gaussian processes (GP).
The Wasserstein-Fourier Distance for Stationary Time Series
The WF distance operates by calculating the Wasserstein distance between the (normalised) power spectral densities (NPSD) of time series.
Band-Limited Gaussian Processes: The Sinc Kernel
We propose a novel class of Gaussian processes (GPs) whose spectra have compact support, meaning that their sample trajectories are almost-surely band limited.
Compositionally-Warped Gaussian Processes
The Gaussian process (GP) is a nonparametric prior distribution over functions indexed by time, space, or other high-dimensional index set.
Low-pass filtering as Bayesian inference
We propose a Bayesian nonparametric method for low-pass filtering that can naturally handle unevenly-sampled and noise-corrupted observations.
Bayesian Nonparametric Spectral Estimation
Spectral estimation (SE) aims to identify how the energy of a signal (e. g., a time series) is distributed across different frequencies.
Bayesian Learning with Wasserstein Barycenters
We introduce and study a novel model-selection strategy for Bayesian learning, based on optimal transport, along with its associated predictive posterior law: the Wasserstein population barycenter of the posterior law over models.
Learning non-Gaussian Time Series using the Box-Cox Gaussian Process
Gaussian processes (GPs) are Bayesian nonparametric generative models that provide interpretability of hyperparameters, admit closed-form expressions for training and inference, and are able to accurately represent uncertainty.
Spectral Mixture Kernels for Multi-Output Gaussian Processes
Early approaches to multiple-output Gaussian processes (MOGPs) relied on linear combinations of independent, latent, single-output Gaussian processes (GPs).
Recovering Latent Signals from a Mixture of Measurements using a Gaussian Process Prior
The proposed model is validated in the recovery of three signals: a smooth synthetic signal, a real-world heart-rate time series and a step function, where GPMM outperformed the standard GP in terms of estimation error, uncertainty representation and recovery of the spectral content of the latent signal.
Improving Sparsity in Kernel Adaptive Filters Using a Unit-Norm Dictionary
Kernel adaptive filters, a class of adaptive nonlinear time-series models, are known by their ability to learn expressive autoregressive patterns from sequential data.
Initialising Kernel Adaptive Filters via Probabilistic Inference
We present a probabilistic framework for both (i) determining the initial settings of kernel adaptive filters (KAFs) and (ii) constructing fully-adaptive KAFs whereby in addition to weights and dictionaries, kernel parameters are learnt sequentially.
Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels
We introduce the Gaussian Process Convolution Model (GPCM), a two-stage nonparametric generative procedure to model stationary signals as the convolution between a continuous-time white-noise process and a continuous-time linear filter drawn from Gaussian process.
