The problem of phase retrieval, ie, the recovery of a function given the magnitude of its
Fourier transform, arises in various fields of science and engineering, including electron …

Channel pruning is one of the predominant approaches for deep model compression.
Existing pruning methods either train from scratch with sparsity constraints on channels, or …

Vibration analysis is a well-established technique for condition monitoring of rotating
machines as the vibration patterns differ depending on the fault or machine condition …

Deep compression refers to removing the redundancy of parameters and feature maps for
deep learning models. Low-rank approximation and pruning for sparse structures play a vital …

Put Predictive Analytics into ActionLearn the basics of Predictive Analysis and Data Mining
through an easy to understand conceptual framework and immediately practice the concepts …

This paper treats the problem of minimizing a general continuously differentiable function
subject to sparsity constraints. We present and analyze several different optimality criteria …

We study network pruning which aims to remove redundant channels/kernels and hence
speed up the inference of deep networks. Existing pruning methods either train from scratch …

This chapter provides an accessible introduction for point processes, and especially Hawkes
processes, for modeling discrete, inter-dependent events over continuous time. We start by …

The use of M-estimators in generalized linear regression models in high dimensional
settings requires risk minimization with hard L_0 constraints. Of the known methods, the …

The principal limitation in many areas of astronomy, especially for directly imaging
exoplanets, arises from instability in the point spread function (PSF) delivered by the …