This project demonstrates the implementation of a Variational Autoencoder (VAE) using TensorFlow and Keras on the MNIST dataset. The VAE is a generative model that learns to encode input data into a ...

Abstract: Transformer outages significantly impact the reliability and cost efficiency of power systems. Studies indicate that approximately 30% of transformer failures stem from issues with on-load ...

Abstract: We introduce a new convolutional autoencoder architecture for user modeling and recommendation tasks with several improvements over the state of the art. First, our model has the flexibility ...

Predicts velocity and pressure fields for various Reynolds numbers. Integrates CAE for dimensionality reduction and reconstruction. Uses LSTM to capture temporal dynamics for short-term predictions.

Introduction: Effective underwater vision is critical for real-time marine ecosystem observation and conservation, especially for autonomous underwater vehicles (AUVs) operating in challenging oceanic ...