Essentially all cells in an organism's body have the same genetic blueprint, or genome, but the set of genes that are ...

A new study discovers a pivotal moment when AI starts comprehending what was read versus relying on the position of the words in a sentence.

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 bottleneck.

In this work, we propose an OOD-aware probabilistic deep MIL model that combines the latent representation from a variational autoencoder and an attention mechanism. At test time, the latent ...

Variational Autoencoders and Probabilistic Latent Representations (VAE) This implementation presents a Variational Autoencoder (VAE) using PyTorch, applied to the MNIST handwritten digit dataset. VAEs ...

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 ...

Specifically, autoencoders by their very nature try to reconstruct an input, which may make them susceptible to overfitting to the identity of a stimulus (Steck, 2020). In the extreme case that an ...

To this end, we propose a multi-domain variational autoencoder framework consisting of multiple domain-specific branches and a latent space shared across all branches for cross-domain information ...