
Fraunhofer uncertainty metrics for classification tasks
Dockerized AI4EU Acumos component for uncertainty estimation for classification networks
The Verifiable AI objectives are organized in four open research questions that constitute four dimensions of the grand challenge resulting from the emergent use of AI in safety-critical applications. These four dimensions also represent the natural way to organize the background material on Verifiable AI:
Dockerized AI4EU Acumos component for uncertainty estimation for classification networks
Verifiable and Explainable RIsk Forecasting Artificial Intelligence Framework
State-of-the-art solver for logic programming under the answer set semantics.
Current solutions to legal translation are based on machine learning techniques. Such techniques are based on statistics and always exhibit a margin of error. In addition, machine learning, which is based on data and algorithms, is by definition unethical...
A data set of 380 million measurements from a hydraulic pump that can be mounted on an industrial robot, for example, to pump liquid paint for spray painting. The data set spans two months in 1-second intervals.
This tutorial series offers a hands-on guide to fairness-aware machine learning that targets beginners in Fair-ML.
A data set of 1.8 billion measurements from a mechanical wrist with three axes that can hold tools, for example, for spray painting in combination with a pump. The data set spans six months in 1-second intervals.
A novel progressive training algorithm for learning Graph Neural Networks with Differential Privacy guarantees