A Survey on Uncertainty Toolkits for Deep Learning

no code implementations • 2 May 2022 • Maximilian Pintz, Joachim Sicking, Maximilian Poretschkin, Maram Akila

The success of deep learning (DL) fostered the creation of unifying frameworks such as tensorflow or pytorch as much as it was driven by their creation in return.

Uncertainty Quantification

A Novel Regression Loss for Non-Parametric Uncertainty Optimization

no code implementations • pproximateinference AABI Symposium 2021 • Joachim Sicking, Maram Akila, Maximilian Pintz, Tim Wirtz, Asja Fischer, Stefan Wrobel

One of the most commonly used approaches so far is Monte Carlo dropout, which is computationally cheap and easy to apply in practice.

regression

Wasserstein Dropout

1 code implementation • 23 Dec 2020 • Joachim Sicking, Maram Akila, Maximilian Pintz, Tim Wirtz, Asja Fischer, Stefan Wrobel

Despite of its importance for safe machine learning, uncertainty quantification for neural networks is far from being solved.

Object Detection regression +1

DenseHMM: Learning Hidden Markov Models by Learning Dense Representations

1 code implementation • 17 Dec 2020 • Joachim Sicking, Maximilian Pintz, Maram Akila, Tim Wirtz

We propose two optimization schemes that make use of this: a modification of the Baum-Welch algorithm and a direct co-occurrence optimization.