Data compression is an essential phase in training a network. The idea is to compress the data so that the same amount of information can be represented by fewer bits. This also helps with the problem ...

This repository implements a Variational Autoencoder (VAE) using PyTorch, inspired by the referenced Kaggle project, with the goal of learning a smooth latent representation of images and generating ...

Abstract: Variational autoencoder is a very concise and effective unsupervised learning method, which can achieve excellent performance when applied in the field of recommendation systems. At present, ...

This repository presents a clean and concise implementation of a Variational Autoencoder (VAE) using PyTorch. VAEs are powerful generative models capable of learning a compressed, continuous latent ...

Variational Autoencoders (VAEs) are an artificial neural network architecture to generate new data. They are similar to regular autoencoders, which consist of an encoder and decoder. The encoder takes ...

Welcome to the first installment of our blog series on diffusion models. In this series, we'll delve into the intricate world of these powerful generative tools, starting with a crucial component: ...

Abstract: Preference-based reinforcement learning (PbRL) enables agents to learn from human feedback without explicit reward engineering. However, existing methods rely on simple MLP architectures ...

Beijing Zhongke Journal Publising Co. Ltd. Music generation is a key use of AI for arts, and is arguably one of the earliest forms of AI art. However, contemporary generative music models rely ...