Batch normalization and dropout for stability Mixed precision training for efficiency Learning rate scheduling and gradient clipping β-VAE support for disentangled representations ...

This paper is a valuable step in multi-subject behavioral modeling using an extension of the Variational Autoencoder (VAE) framework. Using a novel partition of the latent space and in tandem with a ...

Now let’s technically explain it. In the field of AI, when you want to know what the model wants, just look at its loss function. In a VAE, the loss function gives a penalty when you generate an ...

In large public multi-site fMRI datasets, the sample characteristics, data acquisition methods, and MRI scanner models vary across sites and datasets. This non-neural variability obscures neural ...

Abstract: In the context of modern digital transactions, the sheer volume and velocity of transactions necessitate robust financial fraud detection mechanisms to effectively distinguish between ...

Researchers at New York University have developed a new architecture for diffusion models that improves the semantic representation of the images they generate. “Diffusion Transformer with ...