Abstract: Vector quantized variational autoencoders, as variants of variational autoencoders, effectively capture discrete representations by quantizing continuous latent spaces and are widely used in ...
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 ...
Abstract: Autoencoder models of source code are an emerging alternative to autoregressive large language models with important benefits for genetic improvement of software. We hypothesize that encoder ...
Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems, School of Engineering, Huzhou University, Huzhou 313000, PR China Zhejiang Key Laboratory of Industrial Solid ...
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