This repository contains a comprehensive implementation of Variational Autoencoders (VAEs) applied to two different image datasets: CIFAR-10 and Fashion-MNIST. The project demonstrates how dataset ...

Swift for TensorFlow was an experiment in the next-generation platform for machine learning, incorporating the latest research across machine learning, compilers, differentiable programming, systems ...

For the matrix compression problem, a transformer-based vector-quantized variational autoencoder (TVQ-VAE) algorithm is designed to achieve a high ratio compression. Simulation results show that the ...

the model uses a pretrained 3D variational autoencoder to encode both the input video and the reference image. These latents are patchified, concatenated, and fed into the DiT, which processes them ...

We also employ a variational autoencoder to reconstruct latent representations of non-imaging features aligned with imaging features. Based on this, we introduce Adaptive Cross-Modal Graph Learning ...

The image is first encoded by a variational autoencoder (VAE) into a lower-dimensional latent space. The latent image is then divided into a grid of patches, & each patch is flattened into a vector.

Hylleraas Centre for Quantum Molecular Sciences, Department of Chemistry, University of Oslo, P.O. Box 1033, Blindern, Oslo 0315, Norway Department of Chemistry, University of Copenhagen, Copenhagen ...