Capsule networks can effectively model the spatial hierarchical relationships among features; however, the dynamic routing mechanism introduces a large number of parameters and high computational ...

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 ...

Abstract: The capability of ML (Machine Learning) algorithms to recognize images of handwritten numerals is known as HDR (Handwritten Digit Recognition). Because handwritten numerals are imperfect and ...

In the last few years, rapid progress has been unfolding in machine learning (ML) due to the release of specialized datasets that serve as experimental testbeds and public benchmarks, thus focusing ...

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 ...

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 ...

The classification performance of all-optical Convolutional Neural Networks (CNNs) is greatly influenced by components’ misalignment and translation of input images in the practical applications. In ...

HealthTree Cure Hub: A Patient-Derived, Patient-Driven Clinical Cancer Information Platform Used to Overcome Hurdles and Accelerate Research in Multiple Myeloma Adversarial images represent a ...

import numpy as np import cv2 import matplotlib.pyplot as plt #detecting license plate on the vehicle plateCascade = cv2.CascadeClassifier('indian_license_plate.xml') def plate_detect(img): plateImg = ...

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 ...