Search Results for author:
Found 15 papers, 3 papers with code
Multi-Agent Neural Rewriter for Vehicle Routing with Limited Disclosure of Costs
We propose the introduction of a so-called pool in the system which serves as a collection point for unvisited nodes.
Tailored Uncertainty Estimation for Deep Learning Systems
Uncertainty estimation bears the potential to make deep learning (DL) systems more reliable.
Validation of Simulation-Based Testing: Bypassing Domain Shift with Label-to-Image Synthesis
We propose a novel framework consisting of a generative label-to-image synthesis model together with different transferability measures to inspect to what extent we can transfer testing results of semantic segmentation models from synthetic data to equivalent real-life data.
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
Our paper addresses both machine learning experts and safety engineers: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods.
Plants Don't Walk on the Street: Common-Sense Reasoning for Reliable Semantic Segmentation
Data-driven sensor interpretation in autonomous driving can lead to highly implausible predictions as can most of the time be verified with common-sense knowledge.
Street-Map Based Validation of Semantic Segmentation in Autonomous Driving
Lastly, we present quantitative results on the Cityscapes dataset indicating that our validation approach can indeed uncover errors in semantic segmentation masks.
Supporting verification of news articles with automated search for semantically similar articles
We find that our approach is agnostic to concept drifts, i. e. the machine learning task is independent of the hypotheses in a text.
A Novel Regression Loss for Non-Parametric Uncertainty Optimization
One of the most commonly used approaches so far is Monte Carlo dropout, which is computationally cheap and easy to apply in practice.
Wasserstein Dropout
Despite of its importance for safe machine learning, uncertainty quantification for neural networks is far from being solved.
DenseHMM: Learning Hidden Markov Models by Learning Dense Representations
We propose two optimization schemes that make use of this: a modification of the Baum-Welch algorithm and a direct co-occurrence optimization.
Towards Map-Based Validation of Semantic Segmentation Masks
Artificial intelligence for autonomous driving must meet strict requirements on safety and robustness.
Characteristics of Monte Carlo Dropout in Wide Neural Networks
Monte Carlo (MC) dropout is one of the state-of-the-art approaches for uncertainty estimation in neural networks (NNs).
Efficient Decentralized Deep Learning by Dynamic Model Averaging
We propose an efficient protocol for decentralized training of deep neural networks from distributed data sources.
