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.
This accuracy is approximately equal to state-of-the-art Deep Learning optimization procedures.
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.
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.
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.
We present a process-based approach that combines multi-level and multi-modal explanations.
In this set measurement of gender bias is solely based on the translation of occupations.
Such near misses have been proposed by Winston (1970) as efficient guidance for learning in relational domains.
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.
Finally, we quantify these visual explanations based on a bounding-box method with respect to facial regions.
Quick-Shift resulted in the least and Compact-Watershed in the highest correspondence with the reference relevance areas.
First, we show that our approach is capable of identifying a single relation as important explanatory construct.
Explicit models of the environment can be learned to augment such a value function.