In geospatial planning, it is often essential to represent objects in a
...
Segmentation uncertainty models predict a distribution over plausible
se...
Out of distribution (OOD) medical images are frequently encountered, e.g...
We present a novel pipeline for learning the conditional distribution of...
After the recent ground-breaking advances in protein structure predictio...
A wide variety of model explanation approaches have been proposed in rec...
The body of research on classification of solar panel arrays from aerial...
A lot of Machine Learning (ML) and Deep Learning (DL) research is of an
...
We present simple methods for out-of-distribution detection using a trai...
Stochastic latent variable models (LVMs) achieve state-of-the-art perfor...
We present a method to fit exact Gaussian process models to large datase...
We revisit the theory of importance weighted variational inference (IWVI...
Energy-based models (EBMs) provide an elegant framework for density
esti...
Algorithms involving Gaussian processes or determinantal point processes...
In this paper, we introduce a novel combination of Bayesian Models (BMs)...
Deep generative models have shown themselves to be state-of-the-art dens...
We introduce the sequential neural posterior and likelihood approximatio...
When a missing process depends on the missing values themselves, it need...
Generative Adversial Networks (GANs) have made a major impact in compute...
We present a novel family of deep neural architectures, named partially
...
We consider the problem of handling missing data with deep latent variab...
We present a simple technique to train deep latent variable models (DLVM...
Deep latent variable models combine the approximation abilities of deep
...
Deep latent variable models combine the approximation abilities of deep
...
In this paper, we study the trade-offs of different inference approaches...
We present a fast variational Bayesian algorithm for performing non-nega...
Circular variables arise in a multitude of data-modelling contexts rangi...