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

We prove identifiability of a broad class of deep latent variable models that (a) have
universal approximation capabilities and (b) are the decoders of variational autoencoders …

Owing to its nature of scalability and privacy by design, federated learning (FL) has received
increasing interest in decentralized deep learning. FL has also facilitated recent research on …

Model-based data analysis of whole-brain dynamics links the observed data to model
parameters in a network of neural masses. Recently, studies focused on the role of regional …

Variational autoencoders model high-dimensional data by positinglow-dimensional latent
variables that are mapped through a flexibledistribution parametrized by a neural network …

Variational Auto-Encoders (VAEs) are deep latent space generative models which have
been immensely successful in many applications such as image generation, image …

This work identifies the existence and cause of a type of posterior collapse that frequently
occurs in the Bayesian deep learning practice. For a general linear latent variable model …

To learn camera-view invariant features for person Re-IDentification (Re-ID), the cross-
camera image pairs of each person play an important role. However, such cross-view …

In this study, we propose a novel unsupervised Change Detection (CD) model to detect
flood extent using Synthetic Aperture Radar (SAR) time series data. The proposed model is …

Variational autoencoders (VAEs) are a popular framework for modeling complex data
distributions; they can be efficiently trained via variational inference by maximizing the …