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Proximal gradient methods for learning

Known as: Proximal gradient 
Proximal gradient (forward backward splitting) methods for learning is an area of research in optimization and statistical learning theory which… 
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Related topics

Related topics

19 relations
Convex analysisConvex conjugateConvex functionConvex optimization

Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2018
2018
Block Coordinate Proximal Gradient Method for Nonconvex Optimization Problems : Convergence Analysis
We propose a block coordinate proximal gradient method for a composite minimization problem with two nonconvex function… 
2018
2018
Structural Local Sparse Tracking Method Based on Multi-feature Fusion and Fractional Differential
. In view of incomplete using single feature to describe the objects of interest and effectiveness of local sparse representation… 
2018
2018
Trusted Neural Networks for Safety-Constrained Autonomous Control
We propose Trusted Neural Network (TNN) models, which are deep neural network models that satisfy safety constraints critical to… 
2018
2018
Comparison of SIFT and ASIFT based filters for better recognition and tracking in a remote scene environment
In a remote scene environment consisting of multiple objects and miscellaneous scenarios, detecting an object of interest is a… 
Review
2017
Review
2017
Image deblurring in the presence of salt-and-pepper noise
This work addresses image recovery problem in the presence of salt-and-pepper noise and image blur. The salt-and-pepper noise… 
2017
2017
Perturbation resilience of proximal gradient algorithm for composite objectives
In this paper, we study the perturbation resilience of a proximal gradient algorithm under the general Hilbert space setting… 
2016
2016
When Sensor Meets Tensor: Filling Missing Sensor Values Through a Tensor Approach
In the era of the Internet of Things, enormous number of sensors have been deployed in different locations, generating massive… 
2016
2016
Training Ternary Neural Networks with Exact Proximal Operator
In this paper, we propose a stochastic proximal gradient method to train ternary weight neural networks (TNN). The proposed… 
2014
2014
Data-driven scene understanding by adaptive exemplar retrieval
This article studies a data-driven approach for semantically scene understanding, without pixelwise annotation and classifier pre… 
Highly Cited
2010
Highly Cited
2010
Optimum Subspace Learning and Error Correction for Tensors
Confronted with the high-dimensional tensor-like visual data, we derive a method for the decomposition of an observed tensor into… 
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