Proximal operator

In mathematical optimization, the proximal operator is an operator associated with a convex function defined by: It is frequently used in…

Papers overview

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2020

This paper introduces two simple techniques to improve off-policy Reinforcement Learning (RL) algorithms. First, we formulate off…

2019

Recent advances have illustrated that it is often possible to learn to solve linear inverse problems in imaging using training…

2017

In the past decade, sparsity-driven methods have led to substantial improvements in the capabilities of numerous imaging systems…

2017

In ultrasound (US) imaging, beamforming is usually separated from the deconvolution or some other post-processing techniques. The…

2016

In this paper, we propose a stochastic proximal gradient method to train ternary weight neural networks (TNN). The proposed…

2016

For the past few years, researchers hold a strong interests on knowledge-aided object-oriented high-resolution microwave imaging…

2016

2015

Dictionary learning often use ℓp-norm (p <; 2) for sparsity. In this paper, we use a different structure of regularizer, i.e…

2015

For completeness, in this section we derive the dual (5) to the problem of computing proximal operator for the ERM objective (3).

2014