
Sparse Continuous Distributions and FenchelYoung Losses
Exponential families are widely used in machine learning; they include m...
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Distributed Picard Iteration: Application to Distributed EM and Distributed PCA
In recent work, we proposed a distributed Picard iteration (DPI) that al...
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Distributed Picard Iteration
The Picard iteration is widely used to find fixed points of locally cont...
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TimeSHAP: Explaining Recurrent Models through Sequence Perturbations
Recurrent neural networks are a standard building block in numerous mach...
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Control with adaptive Qlearning
This paper evaluates adaptive Qlearning (AQL) and singlepartition adap...
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Variational Mixture of Normalizing Flows
In the past few years, deep generative models, such as generative advers...
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Equilibrium Propagation for Complete Directed Neural Networks
Artificial neural networks, one of the most successful approaches to sup...
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Sparse and Continuous Attention Mechanisms
Exponential families are widely used in machine learning; they include m...
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A ClassificationBased Approach to SemiSupervised Clustering with Pairwise Constraints
In this paper, we introduce a neural network framework for semisupervis...
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Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation
The Conditional Random Field as a Recurrent Neural Network layer is a re...
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Image Restoration Using Conditional Random Fields and Scale Mixtures of Gaussians
This paper proposes a general framework for internal patchbased image r...
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External PatchBased Image Restoration Using Importance Sampling
This paper introduces a new approach to patchbased image restoration ba...
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Impulsive Noise Robust Sparse Recovery via Continuous Mixed Norm
This paper investigates the problem of sparse signal recovery in the pre...
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Poisson Image Denoising Using Best Linear Prediction: A Postprocessing Framework
In this paper, we address the problem of denoising images degraded by Po...
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SceneAdapted PlugandPlay Algorithm with Guaranteed Convergence: Applications to Data Fusion in Imaging
The recently proposed plugandplay (PnP) framework allows leveraging re...
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Blind image deblurring using classadapted image priors
Blind image deblurring (BID) is an illposed inverse problem, usually ad...
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Classspecific image denoising using importance sampling
In this paper, we propose a new image denoising method, tailored to spec...
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Classspecific Poisson denoising by patchbased importance sampling
In this paper, we address the problem of recovering images degraded by P...
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Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation
Many modern computer vision and machine learning applications rely on so...
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Sceneadapted plugandplay algorithm with convergence guarantees
Recent frameworks, such as the socalled plugandplay, allow us to leve...
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Image Restoration with Locally Selected ClassAdapted Models
Stateoftheart algorithms for imaging inverse problems (namely deblurr...
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Image Restoration and Reconstruction using Variable Splitting and Classadapted Image Priors
This paper proposes using a Gaussian mixture model as a prior, for solvi...
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The Ordered Weighted ℓ_1 Norm: Atomic Formulation, Projections, and Algorithms
The ordered weighted ℓ_1 norm (OWL) was recently proposed, with two diff...
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Groupsparse Matrix Recovery
We apply the OSCAR (octagonal selection and clustering algorithms for re...
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A novel sparsity and clustering regularization
We propose a novel SPARsity and Clustering (SPARC) regularizer, which is...
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Solving OSCAR regularization problems by proximal splitting algorithms
The OSCAR (octagonal selection and clustering algorithm for regression) ...
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Alternating Directions Dual Decomposition
We propose AD3, a new algorithm for approximate maximum a posteriori (MA...
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Deconvolving Images with Unknown Boundaries Using the Alternating Direction Method of Multipliers
The alternating direction method of multipliers (ADMM) has recently spar...
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Online Multiple Kernel Learning for Structured Prediction
Despite the recent progress towards efficient multiple kernel learning (...
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Mário A. T. Figueiredo
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Distinguished Professor, Instituto Superior Técnico, University of Lisbon, Portugal.
Signal processing, machine learning, optimization.