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Found 7 papers, 3 papers with code
Point Processes and spatial statistics in time-frequency analysis
The zeros of the spectrogram of a noisy signal are then the zeros of a random analytic function, hence forming a Point Process in $\mathbb{C}$.
Covid19 Reproduction Number: Credibility Intervals by Blockwise Proximal Monte Carlo Samplers
The originality of the devised algorithms stems from combining a Langevin Monte Carlo sampling scheme with Proximal operators.
Temporal evolution of the Covid19 pandemic reproduction number: Estimations from proximal optimization to Monte Carlo sampling
Yet, the assessment of the pandemic intensity within the pandemic period remains a challenging task because of the limited quality of data made available by public health authorities (missing data, outliers and pseudoseasonalities, notably), that calls for cumbersome and ad-hoc preprocessing (denoising) prior to estimation.
A covariant, discrete time-frequency representation tailored for zero-based signal detection
Recent work in time-frequency analysis proposed to switch the focus from the maxima of the spectrogram toward its zeros, which, for signals corrupted by Gaussian noise, form a random point pattern with a very stable structure leveraged by modern spatial statistics tools to perform component disentanglement and signal detection.
Hyperparameter selection for Discrete Mumford-Shah
This work focuses on a parameter-free joint piecewise smooth image denoising and contour detection.
Nonsmooth convex optimization to estimate the Covid-19 reproduction number space-time evolution with robustness against low quality data
The present work aims to overcome these limitations by carefully crafting a functional permitting to estimate jointly, in a single step, the reproduction number and outliers defined to model low quality data.
Automated data-driven selection of the hyperparameters for Total-Variation based texture segmentation
This work extends the Stein's Unbiased GrAdient estimator of the Risk of Deledalle et al. to the case of correlated Gaussian noise, deriving a general automatic tuning of regularization parameters.
