Search Results for author:
Found 18 papers, 7 papers with code
Benchmarking multi-component signal processing methods in the time-frequency plane
For instance, detection and denoising based on the zeros of the spectrogram have been proposed since 2015, contrasting with a long history of focusing on larger values of the spectrogram.
Normalizing flow sampling with Langevin dynamics in the latent space
Normalizing flows (NF) use a continuous generator to map a simple latent (e. g. Gaussian) distribution, towards an empirical target distribution associated with a training data set.
Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inference
This paper introduces a stochastic plug-and-play (PnP) sampling algorithm that leverages variable splitting to efficiently sample from a posterior distribution.
Sliced-Wasserstein normalizing flows: beyond maximum likelihood training
Despite their advantages, normalizing flows generally suffer from several shortcomings including their tendency to generate unrealistic data (e. g., images) and their failing to detect out-of-distribution data.
Learning Optimal Transport Between two Empirical Distributions with Normalizing Flows
Each of these functions is associated to one sub-flow of the network, whose output provides intermediate steps of the transport between the original and target measures.
High-dimensional Gaussian sampling: a review and a unifying approach based on a stochastic proximal point algorithm
In addition to this state of the art, this paper proposes a unifying Gaussian simulation framework by deriving a stochastic counterpart of the celebrated proximal point algorithm in optimization.
Computation
Quantitative inference of the $H_2$ column densities from 3 mm molecular emission: A case study towards Orion B
We aim to use multi-molecule line emission to infer NH2 from radio observations.
Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
Kernel interpolation with continuous volume sampling
A fundamental task in kernel methods is to pick nodes and weights, so as to approximate a given function from an RKHS by the weighted sum of kernel translates located at the nodes.
Kernel quadrature with DPPs
We study quadrature rules for functions from an RKHS, using nodes sampled from a determinantal point process (DPP).
Asymptotically exact data augmentation: models, properties and algorithms
In a broader perspective, this paper shows that AXDA models can benefit from interesting statistical properties and yield efficient inference algorithms.
A determinantal point process for column subset selection
We give bounds on the ratio of the expected approximation error for this DPP over the optimal error of PCA.
A complete framework for linear filtering of bivariate signals
A complete framework for the linear time-invariant (LTI) filtering theory of bivariate signals is proposed based on a tailored quaternion Fourier transform.
Bayesian nonparametric Principal Component Analysis
The selection of the number of significant components is essential but often based on some practical heuristics depending on the application.
Spectral analysis of stationary random bivariate signals
This spectral density can be meaningfully interpreted in terms of frequency-dependent polarization attributes.
Methodology
Time-frequency analysis of bivariate signals
The resulting spectrograms and scalograms provide meaningful representations of both the time-frequency and geometrical/polarization content of the signal.
Methodology
Bayesian anti-sparse coding
Then this probability distribution is used as a prior to promote anti-sparsity in a Gaussian linear inverse problem, yielding a fully Bayesian formulation of anti-sparse coding.
Distributed dictionary learning over a sensor network
We consider the problem of distributed dictionary learning, where a set of nodes is required to collectively learn a common dictionary from noisy measurements.
