Abstract: In the ongoing era of noisy intermediate scaled quantum computers, one of the possible applications to search for an advantage of quantum computing is machine learning. Here we report about ...

Abstract: Background: Machine learning (ML) privacy problems have prompted the creation of privacy-preserving methods, one of which is Federated Learning (FL), which has emerged as an important ...

Department of Bioengineering, University of Illinois, Urbana−Champaign, Illinois 61801, United States Department of Chemical and Biomolecular Engineering, University of Illinois, Urbana−Champaign, ...

This project involves the classification of handwritten digits using three different classifiers: Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), and Decision Trees. The goal is to ...

Multiclass classification is of great interest for various applications, for example, it is a common task in computer vision, where one needs to categorize an image into three or more classes. Here we ...

Dr. James McCaffrey of Microsoft Research details the "Hello World" of image classification: a convolutional neural network (CNN) applied to the MNIST digits dataset. The "Hello World" of image ...

The memristor-based convolutional neural network (CNN) gives full play to the advantages of memristive devices, such as low power consumption, high integration density, and strong network recognition ...

The MNIST dataset is essential for beginners in deep learning, particularly for image classification tasks. Variants of MNIST include KMNIST, QKMNIST, EMNIST, binarized MNIST, and 3D MNIST, catering ...