Computer vision is the art of teaching a computer to see. For example, it could involve building a model to classify whether a photo is of a cat or a dog (binary classification). Or whether a photo is ...

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 ...

The MNIST handwritten digits classification problem involves recognizing digits (0–9) from grayscale images. The MNIST dataset is a benchmark dataset for image classification tasks, particularly ...

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 ...

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 ...

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 ...

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 ...

Abstract: Image classification finds its suitability in applications ranging from medical diagnostics to autonomous vehicles. The existing architectures are computationally exhaustive, complex and ...

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 ...