Frontiers

A resource-efficient quantum convolutional neural network

Quantum Convolutional Neural Network (QCNN) has achieved significant success in solving various complex problems, such as quantum many-body physics and image recognition. In comparison to the ...
GitHub

The MNIST-1D Dataset

Most machine learning models get around the same ~99% test accuracy on MNIST. Our dataset, MNIST-1D, is 100x smaller (default sample size: 4000+1000; dimensionality: 40) and does a better job of ...
IEEE

A Deep Learning Model for Multiclass Image Classification Using Quantum CNN

Abstract: Technological developments in machine learning have opened up new avenues of advancement almost in every sector. The main challenge is communicating effectively with machines and enhancing ...
GitHub

An Architecture Combining Convolutional Neural Network (CNN) and Linear Support Vector ...

This project was inspired by Y. Tang's Deep Learning using Linear Support Vector Machines (2013). The full paper on this project may be read at arXiv.org. The experiments were conducted on a laptop ...
Frontiers

Multiclass classification using quantum convolutional neural networks with hybrid quantum ...

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 ...
Scientific Research Publishing

Creating a Dataset to Boost Civil Engineering Deep Learning Research and Application ()

1 Graduate Transportation Associate, Tennessee Department of Transportation, Nashville, Tennessee, USA. 2 University of Tennessee at Chattanooga, Chattanooga, Tennessee, USA. 3 Transportation Engineer ...