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Found 14 papers, 5 papers with code
Self-Supervised Learning for Image Super-Resolution and Deblurring
These methods critically rely on invariance to translations and/or rotations of the image distribution to learn from incomplete measurement data alone.
Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of data
The main scientific message is that most of the time, training neural networks for predicting extreme heatwaves occurs in a regime of lack of data.
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.
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.
Deep Learning-based Extreme Heatwave Forecast
The present work explores the use of deep learning architectures, trained using outputs of a climate model, as an alternative strategy to forecast the occurrence of extreme long-lasting heatwaves.
Multiview Variational Graph Autoencoders for Canonical Correlation Analysis
We present a novel multiview canonical correlation analysis model based on a variational approach.
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.
$L^γ$-PageRank for Semi-Supervised Learning
A procedure for the automated estimation of the optimal $\gamma$, from a unique observation of data, is devised and assessed.
Bayesian selection for the l2-Potts model regularization parameter: 1D piecewise constant signal denoising
Piecewise constant denoising can be solved either by deterministic optimization approaches, based on the Potts model, or by stochastic Bayesian procedures.
Combining local regularity estimation and total variation optimization for scale-free texture segmentation
Texture segmentation constitutes a standard image processing task, crucial to many applications.
On-the-fly Approximation of Multivariate Total Variation Minimization
In the context of change-point detection, addressed by Total Variation minimization strategies, an efficient on-the-fly algorithm has been designed leading to exact solutions for univariate data.
Bayesian estimation of the multifractality parameter for image texture using a Whittle approximation
Multifractal analysis is a useful signal and image processing tool, yet, the accurate estimation of multifractal parameters for image texture remains a challenge.
