Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using pseudo-inverse training. Compared to other training techniques, such as stochastic gradient descent, ...

Abstract: The sparsity-regularized linear inverse problem has been widely used in many fields, such as remote sensing imaging, image processing and analysis, seismic deconvolution, compressed sensing, ...

Inverse optimisation and linear programming have emerged as crucial instruments in addressing complex decision-making problems where underlying models must be inferred from observed behaviour. At its ...

The LIM assumes the relevant dynamics can be represented as a linear system forced by stochastic noise (Hasselmann, 1988; Penland & Sardeshmukh, 1995), and written in the form of a linear stochastic ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. In this work, we describe the development of a new algorithm for the computation of ...

This repository provides code for the paper "Sampling from the Random Linear Model via Stochastic Localization Up to the AMP Threshold".