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 ...
Abstract: Legged robots face significant challenges in complex terrains due to partial observability. While teacher-student frameworks address this through imitation, they often cause representation ...
Project Overview This project implements a multimodal Interpretable Variational Encoder (IVE) combining MEG signals, video frames, and behavioral covariates into a unified prediction framework. We ...
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 ...
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 ...
Six additional scRNA-seq datasets are reserved for the biological case studies (Sections 3.8–3.10) and excluded from every benchmark statistic: sleep-deprivation bone marrow (GSE280145), a TPO-induced ...
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