Conventional neural networks have been demonstrated to be a powerful framework for
background subtraction in video acquired by static cameras. Indeed, the well-known Self …

Although variational autoencoders (VAEs) represent a widely influential deep generative
model, many aspects of the underlying energy function remain poorly understood. In …

We consider the problem of handling missing data with deep latent variable models
(DLVMs). First, we present a simple technique to train DLVMs when the training set contains …

Abstract Posterior collapse in Variational Autoencoders (VAEs) with uninformative priors
arises when the variational posterior distribution closely matches the prior for a subset of …

Abstract The Variational Autoencoder (VAE) is a powerful architecture capable of
representation learning and generative modeling. When it comes to learning interpretable …

Posterior collapse in Variational Autoencoders (VAEs) arises when the variational
distribution closely matches the uninformative prior for a subset of latent variables. This …

Efforts devoted to mitigate the effects of road traffic congestion have been conducted since
1970s. Nowadays, there is a need for prominent solutions capable of mining information …

We propose an algorithm, guided variational autoencoder (Guided-VAE), that is able to
learn a controllable generative model by performing latent representation disentanglement …

Goal-driven and feedforward-only convolutional neural networks (CNN) have been shown to
be able to predict and decode cortical responses to natural images or videos. Here, we …

Consider an imitation learning problem that the imitator and the expert have different
dynamics models. Most of the current imitation learning methods fail because they focus on …