Mar 4, 2025 · Variational Autoencoders (VAEs) are generative models in machine learning (ML) that create new data similar to the input they are trained on. Along with data generation they also perform common autoencoder tasks like denoising.

Apr 25, 2023 · Variational autoencoders (VAEs) offer a more flexible approach by learning parameters of a distribution of the latent space that can be sampled to generate new data.

A variational autoencoder is a generative model with a prior and noise distribution respectively. Usually such models are trained using the expectation-maximization meta-algorithm (e.g. probabilistic PCA, (spike & slab) sparse coding).

May 1, 2023 · Researchers have proposed several improved versions of autoencoder based on different application fields. First, this paper explains the principle of a conventional autoencoder and investigates the primary development process of an autoencoder. Second, We proposed a taxonomy of autoencoders according to their structures and principles.

Jun 1, 2024 · Our framework integrates Variational Autoencoders (VAE) with Nonlinear Autoregressive with exogenous input (NARX) in a unified identification structure to address overfitting in nonlinear system identification using NARX structures.

Jun 6, 2019 · Variational autoencoders provide a principled framework for learning deep latent-variable models and corresponding inference models. In this work, we provide an introduction to variational autoencoders and some important extensions. Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?)

Jul 8, 2024 · In this blog post, we will start with a quick introduction to the architecture of variational autoencoders and a comparison between variational autoencoders and conventional autoencoders. Next, we will use mathematical expressions and graphics to explain the concepts behind the variational autoencoder network design.

Variational Autoencoders (VAEs) are powerful generative models that merge elements from statistics and information theory with the flexibility offered by deep neural networks to eficiently solve the generation problem for high-dimensional data.

In this work we present an approach for designing variational autoencoders based on evolutionary neural architecture search. Our technique is efficient, avoiding redundant computation, and scalable.

Variational autoencoders (VAE) are neural networks used for the unsuper-vised learning of complicated distributions by using stochastic variational infer-ence. Traditionally, they have been used for image reconstruction or generation.