The thesis defines and evaluates tasks and datasets for automatic fact checking, cite-worthiness, exaggeration detection, and information change measurement in science communication, culminating in SPICED, a cross-media dataset of semantically matched scientific findings.
Investigating the influence of noise and distractors on the interpretation of neural networks
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abstract
Understanding neural networks is becoming increasingly important. Over the last few years different types of visualisation and explanation methods have been proposed. However, none of them explicitly considered the behaviour in the presence of noise and distracting elements. In this work, we will show how noise and distracting dimensions can influence the result of an explanation model. This gives a new theoretical insights to aid selection of the most appropriate explanation model within the deep-Taylor decomposition framework.
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cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Machine Understanding of Scientific Language
The thesis defines and evaluates tasks and datasets for automatic fact checking, cite-worthiness, exaggeration detection, and information change measurement in science communication, culminating in SPICED, a cross-media dataset of semantically matched scientific findings.