Browsing Faculty of Engineering by Author "Julier, S."
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Interpretability of deep learning models: a survey of results
Chakraborty, S.; Tomsett, R.; Raghavendra, R.; Harborne, D.; Alzantot, M.; Cerutti, F.; Srivastava, M.; Preece, A.; Julier, S.; Rao, R. M.; Kelley, T. D.; Braines, D.; Şensoy, Murat; Willis, C. J.; Gurram, P. (IEEE, 2018-06-26)Deep neural networks have achieved near-human accuracy levels in various types of classification and prediction tasks including images, text, speech, and video data. However, the networks continue to be treated mostly as ... -
Misclassification risk and uncertainty quantification in deep classifiers
Şensoy, Murat; Saleki, Maryam; Julier, S.; Aydoğan, Reyhan; Reid, J. (IEEE, 2021)In this paper, we propose risk-calibrated evidential deep classifiers to reduce the costs associated with classification errors. We use two main approaches. The first is to develop methods to quantify the uncertainty of a ... -
Not all mistakes are equal
Şensoy, M.; Saleki, Maryam; Julier, S.; Aydoğan, Reyhan; Reid, J. (The ACM Digital Library, 2020)In many tasks, classifiers play a fundamental role in the way an agent behaves. Most rational agents collect sensor data from the environment, classify it, and act based on that classification. Recently, deep neural networks ...
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