Learned regularization for MRI reconstruction can provide complex data-d...
This paper proposes using a sparse-structured multivariate Gaussian to
p...
Federated learning of deep learning models for supervised tasks, e.g. im...
Multi-task learning requires accurate identification of the correlations...
Deep neural network approaches to inverse imaging problems have produced...
We present a novel approach to Bayesian inference and general Bayesian
c...
Many structured prediction tasks in machine vision have a collection of
...
Gaussian processes (GPs) are nonparametric priors over functions, and fi...
We present an approach to Bayesian Optimization that allows for robust s...
We propose a new framework of imposing monotonicity constraints in a Bay...
The shape of an object is an important characteristic for many vision
pr...
Deep neural networks have recently been used to edit images with great
s...
We present a probabilistic model for unsupervised alignment of
high-dime...
We present a non-parametric Bayesian latent variable model capable of
le...
Variational auto-encoders (VAEs) are a popular and powerful deep generat...
We present a model that can automatically learn alignments between
high-...
This paper is the first work to propose a network to predict a structure...
We would like to learn latent representations that are low-dimensional a...
We introduce Latent Gaussian Process Regression which is a latent variab...
We propose technology to enable a new medium of expression, where video
...
To train good supervised and semi-supervised object classifiers, it is
c...