Abstract The Conditional Neural Process (CNP) family of models offer a promising direction
to tackle few-shot problems by achieving better scalability and competitive predictive …

Codebook collapse is a common problem in training deep generative models with discrete
representation spaces like Vector Quantized Variational Autoencoders (VQ-VAEs). We …

Uncertainty estimation is an important research area to make deep neural networks (DNNs)
more trustworthy. While extensive research on uncertainty estimation has been conducted …

In this paper, we discuss feature engineering for single-pass uncertainty estimation. For
accurate uncertainty estimates, neural networks must extract differences in the feature space …

Neural Stochastic Differential Equations (NSDEs) model the drift and diffusion functions of a
stochastic process as neural networks. While NSDEs are known to make accurate …

A newly-arising uncertainty estimation method named Evidential Deep Learning (EDL),
which can obtain reliable predictive uncertainty in a single forward pass, has garnered …

We study the problem of fitting a model to a dynamical environment when new modes of
behavior emerge sequentially. The learning model is aware when a new mode appears, but …

This thesis focuses on neural network based modeling of stochastic dynamical systems with
applications in the context of autonomous driving. We define three goals for the model that …