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Found 31 papers, 9 papers with code
Proximal Splitting Adversarial Attack for Semantic Segmentation
Classification has been the focal point of research on adversarial attacks, but only a few works investigate methods suited to denser prediction tasks, such as semantic segmentation.
Towards Practical Few-Shot Query Sets: Transductive Minimum Description Length Inference
We relax these assumptions and extend current benchmarks, so that the query-set classes of a given task are unknown, but just belong to a much larger set of possible classes.
Efficient Bayes Inference in Neural Networks through Adaptive Importance Sampling
Bayesian neural networks (BNNs) have received an increased interest in the last years.
Deep Unfolding of the DBFB Algorithm with Application to ROI CT Imaging with Limited Angular Density
We unfold the Dual Block coordinate Forward-Backward (DBFB) algorithm, embedded in an iterative reweighted scheme, allowing the learning of key parameters in a supervised manner.
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 challenging task in image processing.
Proximal Splitting Adversarial Attacks for Semantic Segmentation
Classification has been the focal point of research on adversarial attacks, but only a few works investigate methods suited to denser prediction tasks, such as semantic segmentation.
Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution
In this paper, we introduce a variational Bayesian algorithm (VBA) for image blind deconvolution.
Deep Transform and Metric Learning Networks
Based on its great successes in inference and denosing tasks, Dictionary Learning (DL) and its related sparse optimization formulations have garnered a lot of research interest.
Sparsifying Networks via Subdifferential Inclusion
Sparsifying deep neural networks is of paramount interest in many areas, especially when those networks have to be implemented on low-memory devices.
Learning Maximally Monotone Operators for Image Recovery
Recently, several works have proposed to replace the operator related to the regularization by a more sophisticated denoiser.
Automated Theorem Proving
Image Restoration
Optimization and Control
Image and Video Processing
47H05, 90C25, 90C59, 65K10, 49M27, 68T07, 68U10, 94A08
Sparse Signal Reconstruction for Nonlinear Models via Piecewise Rational Optimization
We propose a method to reconstruct sparse signals degraded by a nonlinear distortion and acquired at a limited sampling rate.
Modeling Electrical Motor Dynamics using Encoder-Decoder with Recurrent Skip Connection
In this paper, we explore the feasibility of modeling the dynamics of an electrical motor by following a data-driven approach, which uses only its inputs and outputs and does not make any assumption on its internal behaviour.
Fixed Point Strategies in Data Science
The goal of this paper is to promote the use of fixed point strategies in data science by showing that they provide a simplifying and unifying framework to model, analyze, and solve a great variety of problems.
Optimization and Control
Deep Transform and Metric Learning Network: Wedding Deep Dictionary Learning and Neural Networks
On account of its many successes in inference tasks and denoising applications, Dictionary Learning (DL) and its related sparse optimization problems have garnered a lot of research interest.
General risk measures for robust machine learning
A wide array of machine learning problems are formulated as the minimization of the expectation of a convex loss function on some parameter space.
Lipschitz Certificates for Neural Network Structures Driven by Averaged Activation Operators
Deriving sharp Lipschitz constants for feed-forward neural networks is essential to assess their robustness in the face of adversarial inputs.
Optimization and Control
Deep Unfolding of a Proximal Interior Point Method for Image Restoration
Variational methods are widely applied to ill-posed inverse problems for they have the ability to embed prior knowledge about the solution.
Deep Neural Network Structures Solving Variational Inequalities
Motivated by structures that appear in deep neural networks, we investigate nonlinear composite models alternating proximity and affine operators defined on different spaces.
Optimization and Control
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-forward neural nets.
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 logistic regression based on a stochastic version of the Douglas-Rachford splitting method.
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) regression problems using a 2D Gaussian Markov random field (GMRF) prior.
Spatially variant PSF modeling in confocal macroscopy
Point spread function (PSF) plays an essential role in image reconstruction.
Image Analysis Using a Dual-Tree $M$-Band Wavelet Transform
We propose a 2D generalization to the $M$-band case of the dual-tree decomposition structure (initially proposed by N. Kingsbury and further investigated by I. Selesnick) based on a Hilbert pair of wavelets.
A Variational Bayesian Approach for Image Restoration. Application to Image Deblurring with Poisson-Gaussian Noise
We demonstrate the potential of the proposed approach through comparisons with state-of-the-art techniques that are specifically tailored to signal recovery in the presence of mixed Poisson-Gaussian noise.
A Proximal Approach for Sparse Multiclass SVM
In this paper, we propose a convex optimization approach for efficiently and exactly solving the multiclass SVM learning problem involving a sparse regularization and the multiclass hinge loss formulated by Crammer and Singer.
A Class of Randomized Primal-Dual Algorithms for Distributed Optimization
Based on a preconditioned version of the randomized block-coordinate forward-backward algorithm recently proposed in [Combettes, Pesquet, 2014], several variants of block-coordinate primal-dual algorithms are designed in order to solve a wide array of monotone inclusion problems.
Optimization and Control 47H05, 49M29, 49M27, 65K10, 90C25
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 processing, computer vision, and machine learning.
A Non-Local Structure Tensor Based Approach for Multicomponent Image Recovery Problems
In this paper, we extend the NLTV-based regularization to multicomponent images by taking advantage of the Structure Tensor (ST) resulting from the gradient of a multicomponent image.
Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI
To improve the performance of the widely used SENSE algorithm, 2D- or slice-specific regularization in the wavelet domain has been deeply investigated.
Primal-dual splitting algorithm for solving inclusions with mixtures of composite, Lipschitzian, and parallel-sum monotone operators
We propose a primal-dual splitting algorithm for solving monotone inclusions involving a mixture of sums, linear compositions, and parallel sums of set-valued and Lipschitzian operators.
Optimization and Control 47H05, 90C25
Proximal Splitting Methods in Signal Processing
The proximity operator of a convex function is a natural extension of the notion of a projection operator onto a convex set.
Optimization and Control Numerical Analysis 90C25, 65K05, 90C90, 94A08
