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10/26/2022
A Variational Inequality Model for Learning Neural Networks
Neural networks have become ubiquitous tools for solving signal and imag...
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10/26/2022
Towards Practical Few-Shot Query Sets: Transductive Minimum Description Length Inference
Standard few-shot benchmarks are often built upon simplifying assumption...
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10/03/2022
Efficient Bayes Inference in Neural Networks through Adaptive Importance Sampling
Bayesian neural networks (BNNs) have received an increased interest in t...
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09/27/2022
Deep Unfolding of the DBFB Algorithm with Application to ROI CT Imaging with Limited Angular Density
This paper addresses the problem of image reconstruction for region-of-i...
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09/03/2022
A CNC approach for Directional Total Variation
The core of many approaches for the resolution of variational inverse pr...
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09/03/2022
A Variational Approach for Joint Image Recovery and Features Extraction Based on Spatially Varying Generalised Gaussian Models
The joint problem of reconstruction / feature extraction is a challengin...
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06/14/2022
Proximal Splitting Adversarial Attacks for Semantic Segmentation
Classification has been the focal point of research on adversarial attac...
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10/14/2021
Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution
In this paper, we introduce a variational Bayesian algorithm (VBA) for i...
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04/21/2021
Deep Transform and Metric Learning Networks
Based on its great successes in inference and denosing tasks, Dictionary...
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10/29/2020
Sparse Signal Reconstruction for Nonlinear Models via Piecewise Rational Optimization
We propose a method to reconstruct sparse signals degraded by a nonlinea...
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02/18/2020
Deep Transform and Metric Learning Network: Wedding Deep Dictionary Learning and Neural Networks
On account of its many successes in inference tasks and denoising applic...
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04/26/2019
General risk measures for robust machine learning
A wide array of machine learning problems are formulated as the minimiza...
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12/11/2018
Deep Unfolding of a Proximal Interior Point Method for Image Restoration
Variational methods are widely applied to ill-posed inverse problems for...
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05/23/2018
A Two-Stage Subspace Trust Region Approach for Deep Neural Network Training
In this paper, we develop a novel second-order method for training feed-...
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12/25/2017
A Random Block-Coordinate Douglas-Rachford Splitting Method with Low Computational Complexity for Binary Logistic Regression
In this paper, we propose a new optimization algorithm for sparse logist...
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09/18/2017
A Fast Algorithm Based on a Sylvester-like Equation for LS Regression with GMRF Prior
This paper presents a fast approach for penalized least squares (LS) reg...
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07/14/2017
Spatially variant PSF modeling in confocal macroscopy
Point spread function (PSF) plays an essential role in image reconstruct...
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02/27/2017
Image Analysis Using a Dual-Tree M-Band Wavelet Transform
We propose a 2D generalization to the M-band case of the dual-tree decom...
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10/24/2016
A Variational Bayesian Approach for Image Restoration. Application to Image Deblurring with Poisson-Gaussian Noise
In this paper, a methodology is investigated for signal recovery in the ...
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06/20/2014
Playing with Duality: An Overview of Recent Primal-Dual Approaches for Solving Large-Scale Optimization Problems
Optimization methods are at the core of many problems in signal/image pr...
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12/23/2011
Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI
Parallel MRI is a fast imaging technique that enables the acquisition of...
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08/26/2011
Noise Covariance Properties in Dual-Tree Wavelet Decompositions
Dual-tree wavelet decompositions have recently gained much popularity, m...
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03/17/2011
