Variational autoencoders (VAEs) are powerful deep generative models widely used to
represent high-dimensional complex data through a low-dimensional latent space learned …

State-of-the-art computer vision systems are trained to predict a fixed set of predetermined
object categories. This restricted form of supervision limits their generality and usability since …

The last decade of machine learning has seen drastic increases in scale and capabilities.
Deep neural networks (DNNs) are increasingly being deployed in the real world. However …

The focus of disentanglement approaches has been on identifying independent factors of
variation in data. However, the causal variables underlying real-world observations are often …

The development of CLIP [Radford et al., 2021] has sparked a debate on whether language
supervision can result in vision models with more transferable representations than …

A common assumption in many domains is that high dimensional data are a smooth
nonlinear function of a small number of independent factors. When is it possible to recover …

In recent years, the rapid development of deep learning approaches has paved the way to
explore the underlying factors that explain the data. In particular, several methods have …

The recently introduced introspective variational autoencoder (IntroVAE) exhibits
outstanding image generations, and allows for amortized inference using an image encoder …

Despite the rapid progress of automatic speech recognition (ASR) technologies targeting
normal speech in recent decades, accurate recognition of dysarthric and elderly speech …

A grand goal in deep learning research is to learn representations capable of generalizing
across distribution shifts. Disentanglement is one promising direction aimed at aligning a …