Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

The use of machine learning in medical and assistive applications is receiving significant
attention thanks to the unique potential it offers to solve complex healthcare problems for …

Recently, the Internet of Things (IoT) has gained a lot of attention, since IoT devices are
placed in various fields. Many of these devices are based on machine learning (ML) models …

Abstract Sparsity in Deep Neural Networks (DNNs) is studied extensively with the focus of
maximizing prediction accuracy given an overall parameter budget. Existing methods rely on …

Classical approaches for one-class problems such as one-class SVM and isolation forest
require careful feature engineering when applied to structured domains like images. State-of …

This paper presents the Edge Learning Machine (ELM), a machine learning framework for
edge devices, which manages the training phase on a desktop computer and performs …

Non-intrusive load monitoring (NILM) helps disaggregate a household's main electricity
consumption to energy usages of individual appliances, greatly cutting down the cost of fine …

Abstract Standard Convolutional Neural Networks (CNNs) designed for computer vision
tasks tend to have large intermediate activation maps. These require large working memory …

Abstract Tiny Machine Learning (TinyML) is an expanding research area based on pushing
intelligence to the edge and bringing machine learning techniques to very small devices and …

The rapid growth of Internet of Things (IoT) and Internet of Vehicles (IoV) are rapidly moving
to the 6G networks, which leads to dramatically raised security issues. Using machine …