Variational Autoencoders (VAEs) are an artificial neural network architecture to generate new data. They are similar to regular autoencoders, which consist of an encoder and decoder. The encoder takes ...

Any quant will tell you that modelling multiple yield curves is no easy task. Most fixed income instruments have numerous term structures, each with 10–15 maturities, that share a hard-to-model, ...

This repository contains the official code and pretrained models for the paper "Learning Linearity in Audio Consistency Autoencoders via Implicit Regularization". This creates a structured latent ...

Abstract: Deep learning models have greatly improved various natural language processing tasks. However, their effectiveness depends on large data sets, which can be difficult to acquire. To mitigate ...

This repository contains the official implementation of LouvreSAE, a lightweight, interpretable method for style representation and style transfer that leverages art-specific Sparse Autoencoders (SAEs ...

Abstract: Predicting information popularity in social networks has become a central focus of network analysis. While recent advancements have been made, most existing approaches rely solely on the ...