
Highlevel Priorbased Loss Functions for Medical Image Segmentation: A Survey
Today, deep convolutional neural networks (CNNs) have demonstrated state...
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Statistical learning for sensor localization in wireless networks
Indoor localization has become an important issue for wireless sensor ne...
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Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks
This paper aims at revisiting Graph Convolutional Neural Networks by bri...
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Une véritable approche ℓ_0 pour l'apprentissage de dictionnaire
Sparse representation learning has recently gained a great success in si...
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Maximum Correntropy Unscented Filter
The unscented transformation (UT) is an efficient method to solve the st...
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Correntropy Maximization via ADMM  Application to Robust Hyperspectral Unmixing
In hyperspectral images, some spectral bands suffer from low signalton...
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BiObjective Nonnegative Matrix Factorization: Linear Versus KernelBased Models
Nonnegative matrix factorization (NMF) is a powerful class of feature ex...
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Entropy of Overcomplete Kernel Dictionaries
In signal analysis and synthesis, linear approximation theory considers ...
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Approximation errors of online sparsification criteria
Many machine learning frameworks, such as resourceallocating networks, ...
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Analyzing sparse dictionaries for online learning with kernels
Many signal processing and machine learning methods share essentially th...
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Kernel Nonnegative Matrix Factorization Without the Curse of the Preimage  Application to Unmixing Hyperspectral Images
The nonnegative matrix factorization (NMF) is widely used in signal and ...
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An eigenanalysis of data centering in machine learning
Many pattern recognition methods rely on statistical information from ce...
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Paul Honeine
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