Variational autoencoders (VAEs) play an important role in high-dimensional data generation based on their ability to fuse the stochastic data representation with the power of recent deep learning techniques. The main advantages of these types of generators lie in their ability to encode the informa...
Normalizing flows, autoregressive models, variational autoencoders (VAEs), and deep energy-based models are among competing likelihood-based frameworks for deep generative learning. Among them, VAEs have the advantage of fast and tractable sampling and easy-to-access encoding networks. However, they...
We present a variational autoencoder (ProteinVAE) that can generate synthetic viral vector serotypes without epitopes for pre-existing neutralizing antibodies. A pre-trained protein language model was incorporated into the encoder to improve data efficiency, and deconvolution-based upsampling was used ...
While the topologies can be modeled and optimized individually, a recent study proposed to use the variational autoencoder (VAE) [1] in the process of topology optimization. The idea behind that is to map the different electric machine topologies into a common latent space, as illustrated in ...
The two most common examples of modern deep generative models are the GAN40 and the Variational AutoEncoder (VAE)41. Both architectures have garnered significant interest in a wide range of fields, ranging from the traditional machine learning subjects of computer vision40 and natural language ...
Lecture 4 Latent Variable Models -- Variational AutoEncoder (VAE) While the old way of doing statistics used to be mostly concerned with inferring what has happened, modern statistics is more concerned with predicting what will happen, and many practical machine learning applications rely on it. ...
is employed as the initial feature input for the variational autoencoder component. Notably, signed graph structural features are leveraged to characterize diseases and microbes. Lastly, based on the representations of diseases and microbes, a multi-class XGBoost classifier is applied to determine the ...
A variational autoencoder (VAE) is one of several generative models that use deep learning to generate new content, detect anomalies and remove noise. VAEs first appeared in 2013, about the same time as other generative AI algorithms, such as generative adversarial networks (GANs) and diffusion...
This research paper introduces VAE-MCRS, a variational autoencoder-based algorithm for multi-criteria recommendation systems. The VAE-MCRS model uses a sequential learning process that adapts its internal representations. The experiment results demonstrate its effectiveness in identifying the relationships bet...
3.2. Variational Inference and Variational Autoencoders Now we describe the basic technique, variational inference (VI), and the building blocks of the proposed framework: variational autoencoders (VAEs). First, we describe the idea of VI for posterior approximation. Let 𝑝𝜃(𝑥|𝑦)pθ(...