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Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics., , , , , , , , , and 3 other author(s). CoRR, (2022)The Parameterized Complexity of Approximate Inference in Bayesian Networks.. Probabilistic Graphical Models, volume 52 of JMLR Workshop and Conference Proceedings, page 264-274. JMLR.org, (2016)A Spiking Neuron Implementation of Genetic Algorithms for Optimization., , , , and . BNAIC/BENELEARN (Selected Papers), volume 1398 of Communications in Computer and Information Science, page 91-105. Springer, (2020)The Computational Complexity of Sensitivity Analysis and Parameter Tuning, and . CoRR, (2012)Predictive coding and the Bayesian brain: Intractability hurdles that are yet to be overcome., and . CogSci, cognitivesciencesociety.org, (2013)Local Monotonicity in Probabilistic Networks., , and . ECSQARU, volume 4724 of Lecture Notes in Computer Science, page 548-559. Springer, (2007)Structure Approximation of Most Probable Explanations in Bayesian Networks.. ECSQARU, volume 7958 of Lecture Notes in Computer Science, page 340-351. Springer, (2013)Finding Dissimilar Explanations in Bayesian Networks: Complexity Results.. BNCAI, volume 1021 of Communications in Computer and Information Science, page 65-72. Springer, (2018)On the computational power and complexity of Spiking Neural Networks., and . NICE, page 4:1-4:7. ACM, (2020)Postponed due to the Covid-19 pandemic..Motivating explanations in Bayesian networks using MAP-independence.. CoRR, (2022)