Search Results for author: Ute Schmid

Found 14 papers, 3 papers with code

Explanatory machine learning for sequential human teaching

1 code implementation20 May 2022 Lun Ai, Johannes Langer, Stephen H. Muggleton, Ute Schmid

We propose a framework for the effects of sequential teaching on comprehension based on an existing definition of comprehensibility and provide evidence for support from data collected in human trials.

BIG-bench Machine Learning Inductive logic programming

Explainable Online Lane Change Predictions on a Digital Twin with a Layer Normalized LSTM and Layer-wise Relevance Propagation

no code implementations4 Apr 2022 Christoph Wehner, Francis Powlesland, Bashar Altakrouri, Ute Schmid

The core implementation includes consuming live data from a digital twin on a German highway, live predictions and explanations of lane changes by extending LRP to layer normalized LSTMs, and an interface for communicating and explaining the predictions to a human user.

Decision Making

An Interactive Explanatory AI System for Industrial Quality Control

no code implementations17 Mar 2022 Dennis Müller, Michael März, Stephan Scheele, Ute Schmid

Machine learning based image classification algorithms, such as deep neural network approaches, will be increasingly employed in critical settings such as quality control in industry, where transparency and comprehensibility of decisions are crucial.

BIG-bench Machine Learning Defect Detection +2

Enabling Verification of Deep Neural Networks in Perception Tasks Using Fuzzy Logic and Concept Embeddings

no code implementations3 Jan 2022 Gesina Schwalbe, Christian Wirth, Ute Schmid

In this work, we present a simple, yet effective, approach to verify that a CNN complies with symbolic predicate logic rules which relate visual concepts.

Explainable artificial intelligence

Beneficial and Harmful Explanatory Machine Learning

no code implementations9 Sep 2020 Lun Ai, Stephen H. Muggleton, Céline Hocquette, Mark Gromowski, Ute Schmid

USML is demonstrated by a measurable increase in human performance of a task following provision to the human of a symbolic machine learned theory for task performance.

BIG-bench Machine Learning Self-Learning

Verifying Deep Learning-based Decisions for Facial Expression Recognition

no code implementations14 Feb 2020 Ines Rieger, Rene Kollmann, Bettina Finzel, Dominik Seuss, Ute Schmid

Finally, we quantify these visual explanations based on a bounding-box method with respect to facial regions.

Facial Expression Recognition

Effect of Superpixel Aggregation on Explanations in LIME -- A Case Study with Biological Data

1 code implementation17 Oct 2019 Ludwig Schallner, Johannes Rabold, Oliver Scholz, Ute Schmid

Quick-Shift resulted in the least and Compact-Watershed in the highest correspondence with the reference relevance areas.

Image Classification Superpixels

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