This project was developed in Jupyter Notebook using Python, TensorFlow, and Keras. MNIST dataset: The dataset contains 60,000 training images and 10,000 test images of handwritten digits (0-9). Each ...

This paper proposes an innovative entropy-aware masked autoencoder framework, namely TMAE, for low-cost traffic flow inference. TMAE leverages a small number of selectively measured regions with few ...

This repository contains a comprehensive implementation of Variational Autoencoders (VAEs) applied to two different image datasets: CIFAR-10 and Fashion-MNIST. The project demonstrates how dataset ...