Patrice Abry
YOU?
Author Swipe
View article: Equivariant Splitting: Self-supervised learning from incomplete data
Equivariant Splitting: Self-supervised learning from incomplete data Open
Self-supervised learning for inverse problems allows to train a reconstruction network from noise and/or incomplete data alone. These methods have the potential of enabling learning-based solutions when obtaining ground-truth references fo…
View article: Self-supervised learning for phase retrieval
Self-supervised learning for phase retrieval Open
In recent years, deep neural networks have emerged as a solution for inverse imaging problems. These networks are generally trained using pairs of images: one degraded and the other of high quality, the latter being called 'ground truth'. …
View article: Multiscale Approximate Eigenvectors for Multivariate Self-Similarity Estimation
Multiscale Approximate Eigenvectors for Multivariate Self-Similarity Estimation Open
International audience
View article: A SCALED POISSON BAYESIAN MODEL FOR VIRAL EPIDEMIC MONITORING
A SCALED POISSON BAYESIAN MODEL FOR VIRAL EPIDEMIC MONITORING Open
Monitoring an ongoing epidemic requires accurate, trustworthy and easy to use tools, capable of handling low quality data. Extending existing epidemiological models quantifying the propagation intensity via a time-varying reproduction numb…
View article: Detecting global financial crises with scarce data by multivariate nonlinear filtering
Detecting global financial crises with scarce data by multivariate nonlinear filtering Open
An original procedure is devised for the automated detection of global financial crises from multivariate databases of share prices. It consists of: i) the construction of time series from the time-windowed estimations of crisis relevant i…
View article: Multifractal analysis based on the weak scaling exponent and applications to MEG recordings in neuroscience
Multifractal analysis based on the weak scaling exponent and applications to MEG recordings in neuroscience Open
We develop the mathematical properties of a multifractal analysis of data based on the weak scaling exponent. The advantage of this analysis is that it does not require any a priori global regularity assumption on the analyzed signal, in c…
View article: A bivariate multifractal analysis approach to understanding socio-spatial segregation dynamics
A bivariate multifractal analysis approach to understanding socio-spatial segregation dynamics Open
Although the study of multifractal properties is now an established approach for the statistical analysis of urban data, the joint multifractal analysis of several spatial signals remains largely unexplored. The latter is crucial for under…
View article: A spectral clustering-type algorithm for the consistent estimation of the Hurst distribution in moderately high dimensions
A spectral clustering-type algorithm for the consistent estimation of the Hurst distribution in moderately high dimensions Open
Scale invariance (fractality) is a prominent feature of the large-scale behavior of many stochastic systems. In this work, we construct an algorithm for the statistical identification of the Hurst distribution (in particular, the scaling e…
View article: On the empirical spectral distribution of large wavelet random matrices based on mixed-Gaussian fractional measurements in moderately high dimensions
On the empirical spectral distribution of large wavelet random matrices based on mixed-Gaussian fractional measurements in moderately high dimensions Open
International audience
View article: Equivariance-Based Self-Supervised Learning for Audio Signal Recovery from Clipped Measurements
Equivariance-Based Self-Supervised Learning for Audio Signal Recovery from Clipped Measurements Open
In numerous inverse problems, state-of-the-art solving strategies involve training neural networks from ground truth and associated measurement datasets that, however, may be expensive or impossible to collect. Recently, self-supervised le…
View article: Identifying High-Dimensional Self-Similarity Based on Spectral Clustering Applied to Large Wavelet Random Matrices
Identifying High-Dimensional Self-Similarity Based on Spectral Clustering Applied to Large Wavelet Random Matrices Open
International audience
View article: Synthetic Spatiotemporal Covid19 Infection Counts to Assess Graph-Regularized Estimation of Multivariate Reproduction Numbers
Synthetic Spatiotemporal Covid19 Infection Counts to Assess Graph-Regularized Estimation of Multivariate Reproduction Numbers Open
International audience
View article: On the empirical spectral distribution of large wavelet random matrices based on mixed-Gaussian fractional measurements in moderately high dimensions
On the empirical spectral distribution of large wavelet random matrices based on mixed-Gaussian fractional measurements in moderately high dimensions Open
In this paper, we characterize the convergence of the (rescaled logarithmic) empirical spectral distribution of wavelet random matrices. We assume a moderately high-dimensional framework where the sample size $n$, the dimension $p(n)$ and,…
View article: Multivariate Selfsimilarity: Multiscale Eigen-Structures for Selfsimilarity Parameter Estimation
Multivariate Selfsimilarity: Multiscale Eigen-Structures for Selfsimilarity Parameter Estimation Open
Scale-free dynamics, formalized by selfsimilarity, provides a versatile paradigm massively and ubiquitously used to model temporal dynamics in real-world data.However, its practical use has mostly remained univariate so far.By contrast, mo…
View article: Scale-Equivariant Imaging: Self-Supervised Learning for Image Super-Resolution and Deblurring
Scale-Equivariant Imaging: Self-Supervised Learning for Image Super-Resolution and Deblurring Open
Self-supervised methods have recently proved to be nearly as effective as supervised ones in various imaging inverse problems, paving the way for learning-based approaches in scientific and medical imaging applications where ground truth d…
View article: Pandemic Intensity Estimation from Stochastic Approximation-Based Algorithms
Pandemic Intensity Estimation from Stochastic Approximation-Based Algorithms Open
International audience
View article: Multivariate selfsimilarity: Multiscale eigen-structures for selfsimilarity parameter estimation
Multivariate selfsimilarity: Multiscale eigen-structures for selfsimilarity parameter estimation Open
Scale-free dynamics, formalized by selfsimilarity, provides a versatile paradigm massively and ubiquitously used to model temporal dynamics in real-world data. However, its practical use has mostly remained univariate so far. By contrast, …
View article: A Robust Model and its EM Algorithm for the Estimation of the Multifractality Parameter
A Robust Model and its EM Algorithm for the Estimation of the Multifractality Parameter Open
Session: Wed PM2.L2: Robust Methods
View article: Epileptic Seizure Prediction from Eigen-Wavelet Multivariate Self-Similarity Analysis of Multi-Channel EEG Signals
Epileptic Seizure Prediction from Eigen-Wavelet Multivariate Self-Similarity Analysis of Multi-Channel EEG Signals Open
Session: Thu PM1.P: Explainability and Interpretability in Biometric and Human-centric Information Processing / Fractal-Wavelet Techniques in Signal Processing
View article: Combining Dual-Tree Wavelet Analysis and Proximal Optimization for Anisotropic Scale-Free Texture Segmentation
Combining Dual-Tree Wavelet Analysis and Proximal Optimization for Anisotropic Scale-Free Texture Segmentation Open
International audience
View article: Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of data
Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of data Open
Understanding extreme events and their probability is key for the study of\nclimate change impacts, risk assessment, adaptation, and the protection of\nliving beings. Forecasting the occurrence probability of extreme heatwaves is a\nprimar…
View article: Probabilistic forecast of extreme heat waves using convolutional neural networks and rare event simulations
Probabilistic forecast of extreme heat waves using convolutional neural networks and rare event simulations Open
Understanding extreme events and their probability is key for the study of climate change impacts, risk assessment, adaptation, and the protection of living beings. Extreme heatwaves are, and likely will be in the future, among the deadlie…
View article: Covid19 Reproduction Number: Credibility Intervals by Blockwise Proximal Monte Carlo Samplers
Covid19 Reproduction Number: Credibility Intervals by Blockwise Proximal Monte Carlo Samplers Open
Monitoring the Covid19 pandemic constitutes a critical societal stake that received considerable research efforts. The intensity of the pandemic on a given territory is efficiently measured by the reproduction number, quantifying the rate …
View article: Multifractal Anomaly Detection in Images via Space-Scale Surrogates
Multifractal Anomaly Detection in Images via Space-Scale Surrogates Open
International audience
View article: Local multifractality in urban systems—the case study of housing prices in the greater Paris region
Local multifractality in urban systems—the case study of housing prices in the greater Paris region Open
Even though the study of fractal and multifractal properties has now become an established approach for statistical urban data analysis, the accurate multifractal characterisation of smaller, district-scale spatial units is still a somewha…