Abstract: As autonomous mobile robots begin to populate public spaces, it is becoming increasingly important for robots to accurately distinguish pedestrians and navigate safely to avoid collisions.

Abstract: Traditional recommender systems, such as collaborative filtering and content filtering, have inherent limitations, including cold start issues and challenges in filtering information.

This project demonstrates the use of a Variational AutoEncoder (VAE) to learn a latent space representation of simple synthetic data: black-and-white images of circles with varying radius, x, and y ...

This repository contains a pytorch (+ pytorch_lightning) implementation of the Variational Fair Autoencoder (VFAE) as proposed in "The Variational Fair Autoencoder". The code was written in order to ...