7 citations · 10 across the 5 of their papers we have counts for
7 papers
Classification via Tensor Decompositions of Echo State Networks
Ashley Prater
This work introduces a tensor-based method to perform supervised classification on spatiotemporal data processed in an echo state network. Typically when performing supervised clas…
Comparison of echo state network output layer classification methods on noisy data
Ashley Prater
Echo state networks are a recently developed type of recurrent neural network where the internal layer is fixed with random weights, and only the output layer is trained on specifi…
Reservoir computing for spatiotemporal signal classification without trained output weights
Ashley Prater
Reservoir computing is a recently introduced machine learning paradigm that has been shown to be well-suited for the processing of spatiotemporal data. Rather than training the net…
A Super-Resolution Framework for Tensor Decomposition
Qiuwei Li, Ashley Prater, Lixin Shen +1
This work considers a super-resolution framework for overcomplete tensor decomposition. Specifically, we view tensor decomposition as a super-resolution problem of recovering a sum…
Finding Dantzig selectors with a proximity operator based fixed-point algorithm
Ashley Prater, Lixin Shen, Bruce W. Suter
In this paper, we study a simple iterative method for finding the Dantzig selector, which was designed for linear regression problems. The method consists of two main stages. The f…
Sparse generalized Fourier series via collocation-based optimization
Ashley Prater
Generalized Fourier series with orthogonal polynomial bases have useful applications in several fields, including differential equations, pattern recognition, and image and signal…