Browsing by Author "Cerutti, F."
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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 ... -
Learning and reasoning in complex coalition information environments: a critical analysis
Cerutti, F.; Alzantot, M.; Xing, T.; Harborne, D.; Bakdash, J. Z.; Braines, D.; Chakraborty, S.; Kaplan, L.; Kimmig, A.; Preece, A.; Raghavendra, R.; Şensoy, Murat; Srivastava, M. (IEEE, 2018-09-05)In this paper we provide a critical analysis with metrics that will inform guidelines for designing distributed systems for Collective Situational Understanding (CSU). CSU requires both collective insight - i.e., accurate ... -
Privacy enforcement through policy extension
Arunkumar, S.; Srivatsa, M.; Soyluoglu, Berker; Şensoy, Murat; Cerutti, F. (IEEE, 2016)Successful coalition operations require contributions from the coalition partners which might have hidden goals and desiderata in addition to the shared coalition goals. Therefore, there is an inevitable risk-utility ... -
Probabilistic logic programming with beta-distributed random variables
Cerutti, F.; Kaplan, L.; Kimmig, A.; Şensoy, Murat (Association for the Advancement of Artificial Intelligence, 2019-07-17)We enable aProbLog-a probabilistic logical programming approach-to reason in presence of uncertain probabilities represented as Beta-distributed random variables. We achieve the same performance of state-of-the-art algorithms ... -
Subjective bayesian networks and human-in-the-loop situational understanding
Braines, D.; Thomas, A.; Kaplan, L.; Şensoy, Murat; Bakdash, J. Z.; Ivanovska, M.; Preece, A.; Cerutti, F. (Springer, 2018-03-21)In this paper we present a methodology to exploit human-machine coalitions for situational understanding. Situational understanding refers to the ability to relate relevant information and form logical conclusions, as well ... -
Uncertainty-aware situational understanding
Tomsett, R.; Kaplan, L.; Cerutti, F.; Sullivan, P.; Vente, D.; Vilamala, M. R.; Kimmig, A.; Preece, A.; Şensoy, Murat (SPIE, 2019)Situational understanding is impossible without causal reasoning and reasoning under and about uncertainty, i.e. prob-abilistic reasoning and reasoning about the confidence in the uncertainty assessment. We therefore ...
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