In recent years, the enormous demand for computing resources resulting from massive data
and complex network models has become the limitation of deep learning. In the large-scale …

While Deep Learning-based Network Intrusion Detection Systems (DL-NIDS) have recently
been significantly explored and shown superior performance, they are insufficient to actively …

This paper aims at enlarging the problem of Neural Architecture Search (NAS) from Single-
Path and Multi-Path Search to automated Mixed-Path Search. In particular, we model the …

In this paper, we focus on the high-dimensional double sparse structures, where the
parameter of interest simultaneously encourages group-wise and element-wise sparsity. By …

In this paper, we focus our attention on the high-dimensional double sparse linear
regression, that is, a combination of element-wise and group-wise sparsity. To address this …

Feature selection reduces the complexity of high-dimensional datasets and helps to gain
insights into systematic variation in the data. These aspects are essential in domains that …

We introduce the notion of self-concordant smoothing for minimizing the sum of two convex
functions: the first is smooth and the second may be nonsmooth. Our framework results …

Abstract Background and Objective Functional brain graph (FBG), by describing the
interactions between different brain regions, provides an effective representation of fMRI …

The deterministic CUR matrix decomposition is a low-rank approximation method to analyze
a data matrix. It has attracted considerable attention due to its high interpretability, which …

Data visualization has become a fundamental process of data engineering. t-SNE is one of
the most popular data visualization approaches. However, its computation cost is quadratic …