Publications associées à Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Estimation Error of the Constrained Lasso

Volkan Cevher, Yen-Huan Li, Ya-Ping Hsieh, Nissim Zerbib

This paper presents a non-asymptotic upper bound for the estimation error of the constrained lasso, under the high-dimensional (

n \ll p

) setting. In contrast to existing results, the error bound in this paper is sharp, is valid when the parameter to be e ...

2016

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Graph Chatbot

Chattez avec Graph Search

Beyond the Pixel-Wise Loss for Topology-Aware Delineation

Applications of Approximate Learning and Inference for Probabilistic Models

Deep Convolutional Neural Network for Inverse Problems in Imaging

Distributed Learning for Stochastic Generalized Nash Equilibrium Problems

Subspace Detection of DNN Posterior Probabilities via Sparse Representation for Query by Example Spoken Term Detection

Sparse Hidden Markov Models for Exemplar-based Speech Recognition Using Deep Neural Network Posterior Features

Estimation Error of the Constrained Lasso

Importance Sampling Tree for Large-scale Empirical Expectation

Principled Parallel Mean-Field Inference for Discrete Random Fields

Sparsity-Driven Statistical Inference for Inverse Problems

Applications of Approximate Learning and Inference for Probabilistic Models

Sparsity-Driven Statistical Inference for Inverse Problems

Principled Parallel Mean-Field Inference for Discrete Random Fields

Beyond the Pixel-Wise Loss for Topology-Aware Delineation

Estimation Error of the Constrained Lasso

Importance Sampling Tree for Large-scale Empirical Expectation

Deep Convolutional Neural Network for Inverse Problems in Imaging

Sparse Hidden Markov Models for Exemplar-based Speech Recognition Using Deep Neural Network Posterior Features

Distributed Learning for Stochastic Generalized Nash Equilibrium Problems

Subspace Detection of DNN Posterior Probabilities via Sparse Representation for Query by Example Spoken Term Detection