IEEE

Hybrid Quantum Network for classification of finance and MNIST data

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 ...
GitHub

MultiBench: Multiscale Benchmarks for Multimodal Representation Learning

Paul Pu Liang, Yiwei Lyu, Xiang Fan, Zetian Wu, Yun Cheng, Jason Wu, Leslie Chen, Peter Wu, Michelle A. Lee, Yuke Zhu, Ruslan Salakhutdinov, Louis-Philippe Morency NeurIPS 2021 Datasets and Benchmarks ...
IEEE

Privacy-Preserving Image Classification based on Federated Learning with Hybrid CNNs Model on MNIST Data

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 ...
GitHub

Using LDA, SVM, and Decision Trees to deal with MNIST data set

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 ...
Frontiers

TAIM: Tool for Analyzing Root Images to Calculate the Infection Rate of Arbuscular Mycorrhizal Fungi

1 Graduate School of Engineering, Osaka Prefecture University, Osaka, Japan 2 Graduate School of Life and Environmental Sciences, Osaka Prefecture University, Osaka, Japan Arbuscular mycorrhizal fungi ...
Visual Studio Magazine

Convolutional Neural Networks for MNIST Data Using PyTorch

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 ...
Visual Studio Magazine

Convolutional Neural Networks for MNIST Data Using PyTorch

The "Hello World" of image classification is a convolutional neural network (CNN) applied to the MNIST digits dataset. A good way to see where this article is headed is to take a look at the ...
Analytics India Magazine

6 MNIST Image Datasets That Data Scientists Should Be Aware Of (With Python Implementation)

In Computer Vision, specifically, Image processing has become more efficient with the use of deep learning algorithms. To show the performance of these neural networks some basic preprocessed datasets ...
Frontiers

Bio-Inspired Techniques in a Fully Digital Approach for Lifelong Learning

Lifelong learning has deeply underpinned the resilience of biological organisms respect to a constantly changing environment. This flexibility has allowed the evolution of parallel-distributed systems ...