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The Odds are Odd: A Statistical Test for Detecting Adversarial Examples., , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 5498-5507. PMLR, (2019)High Performance Space Computing with System-on-Chip Instrument Avionics for Space-based Next Generation Imaging Spectrometers (NGIS)., , , , , , , , , and 7 other author(s). AHS, page 33-36. IEEE, (2018)A Primer on Multi-Neuron Relaxation-based Adversarial Robustness Certification.. CoRR, (2021)Robustness and Regularization of Deep Neural Networks.. ETH Zurich, Zürich, Switzerland, (2021)base-search.net (ftethz:oai:www.research-collection.ethz.ch:20.500.11850/490303).Adversarial Training is a Form of Data-dependent Operator Norm Regularization., , and . NeurIPS, (2020)The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks., , , , , , , , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 9289-9299. PMLR, (2020)Precise characterization of the prior predictive distribution of deep ReLU networks., , , , and . NeurIPS, page 20851-20862. (2021)How Good is the Bayes Posterior in Deep Neural Networks Really?, , , , , , , , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 10248-10259. PMLR, (2020)How Good is the Bayes Posterior in Deep Neural Networks Really?, , , , , , , , , and . (2020)cite arxiv:2002.02405.Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect., , , , and . NeurIPS, page 12738-12748. (2021)