6 citations · 24 across the 8 of their papers we have counts for
8 papers
Task Planning Support for Arborists and Foresters: Comparing Deep Learning Approaches for Tree Inventory and Tree Vitality Assessment Based on UAV-Data
Jonas-Dario Troles, Richard Nieding, Sonia Simons +1
Climate crisis and correlating prolonged, more intense periods of drought threaten tree health in cities and forests. In consequence, arborists and foresters suffer from increasing…
Explaining Hate Speech Classification with Model Agnostic Methods
Durgesh Nandini, Ute Schmid
There have been remarkable breakthroughs in Machine Learning and Artificial Intelligence, notably in the areas of Natural Language Processing and Deep Learning. Additionally, hate…
CAIPI in Practice: Towards Explainable Interactive Medical Image Classification
Emanuel Slany, Yannik Ott, Stephan Scheele +2
Would you trust physicians if they cannot explain their decisions to you? Medical diagnostics using machine learning gained enormously in importance within the last decade. However…
Explainable Online Lane Change Predictions on a Digital Twin with a Layer Normalized LSTM and Layer-wise Relevance Propagation
Christoph Wehner, Francis Powlesland, Bashar Altakrouri +1
Artificial Intelligence and Digital Twins play an integral role in driving innovation in the domain of intelligent driving. Long short-term memory (LSTM) is a leading driver in the…
An Interactive Explanatory AI System for Industrial Quality Control
Dennis Müller, Michael März, Stephan Scheele +1
Machine learning based image classification algorithms, such as deep neural network approaches, will be increasingly employed in critical settings such as quality control in indust…
Enabling Verification of Deep Neural Networks in Perception Tasks Using Fuzzy Logic and Concept Embeddings
Gesina Schwalbe, Christian Wirth, Ute Schmid
One major drawback of deep convolutional neural networks (CNNs) for use in safety critical applications is their black-box nature. This makes it hard to verify or monitor complex,…
Extending Challenge Sets to Uncover Gender Bias in Machine Translation: Impact of Stereotypical Verbs and Adjectives
Jonas-Dario Troles, Ute Schmid
Human gender bias is reflected in language and text production. Because state-of-the-art machine translation (MT) systems are trained on large corpora of text, mostly generated by…
Generating Contrastive Explanations for Inductive Logic Programming Based on a Near Miss Approach
Johannes Rabold, Michael Siebers, Ute Schmid
In recent research, human-understandable explanations of machine learning models have received a lot of attention. Often explanations are given in form of model simplifications or…