Within the past few years, models that can predict the structure or function of proteins have been widely used for a variety of biological applications, such as identifying drug targets and designing ...

Abstract: In the context of histological image classification, Multiple Instance Learning (MIL) methods only require labels at Whole Slide Image (WSI) level, effectively reducing the annotation ...

AI is helping scientists crack the code on next-gen batteries that could replace lithium-ion tech. By discovering novel porous materials, researchers may have paved the way for more powerful and ...

Mechanistic interpretability is emerging as a strategic advantage for businesses looking to deploy AI responsibly.

This implementation presents a Variational Autoencoder (VAE) using PyTorch, applied to the MNIST handwritten digit dataset. VAEs are generative models that learn latent representations of data by ...

ABSTRACT: The integration of artificial intelligence (AI) into the fashion industry is driving transformative advancements in design, production, and sustainability. This article explores the ...

A transfer-learned hierarchical variational autoencoder model for computational design of anticancer peptides.. If you have the appropriate software installed, you can download article citation data ...

However, I think its useful application lies in scenarios where it trains VAEs (Variational Autoencoders) to convert images or audio into latent spaces. As is well-known, GANs (Generative Adversarial ...