Showing 1–20 of 56 results
/ Date/ Name
Dec 16, 2010Linearization effect in multifractal analysis: Insights from the Random Energy ModelNov 7, 2018A Generalized Multifractal Formalism for the Estimation of Nonconcave Multifractal SpectraOct 30, 2020Multiview Variational Graph Autoencoders for Canonical Correlation AnalysisSep 20, 2021Nonsmooth convex optimization to estimate the Covid-19 reproduction number space-time evolution with robustness against low quality dataOct 11, 2019Strongly Convex Optimization for Joint Fractal Feature Estimation and Texture SegmentationMar 6, 2017Scaling in Internet Traffic: a 14 year and 3 day longitudinal study, with multiscale analyses and random projectionsAug 27, 2016Bayesian selection for the l2-Potts model regularization parameter: 1D piecewise constant signal denoisingSep 3, 2024Equivariance-based self-supervised learning for audio signal recovery from clipped measurementsNov 6, 2023Multivariate selfsimilarity: Multiscale eigen-structures for selfsimilarity parameter estimationJun 5, 2020Parameter-free and fast nonlinear piecewise filtering. Application to experimental physicsMar 17, 2021Deep Learning-based Extreme Heatwave ForecastJun 19, 2014General limit distributions for sums of random variables with a matrix product representationJul 10, 2015p-exponent and p-leaders, Part II: Multifractal Analysis. Relations to Detrended Fluctuation AnalysisNov 29, 2017Information Theory to probe Intrapartum Fetal Heart Rate DynamicsApr 22, 2015Combining local regularity estimation and total variation optimization for scale-free texture segmentationFeb 25, 2026Learning to reconstruct from saturated data: audio declipping and high-dynamic range imagingOct 1, 2025Equivariant Splitting: Self-supervised learning from incomplete dataApr 20, 2020Automated data-driven selection of the hyperparameters for Total-Variation based texture segmentationFeb 11, 2022Temporal evolution of the Covid19 pandemic reproduction number: Estimations from proximal optimization to Monte Carlo samplingAug 1, 2022Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of data