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Approximate and Memorize (A&M) : Settling opposing views in replay-based continuous unsupervised domain adaptation.

, , and . Knowl. Based Syst., (2024)

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Acceleration Operators in the Value Iteration Algorithms for Markov Decision Processes., , , and . Oper. Res., 58 (1): 193-202 (2010)Bayesian Experience Reuse for Learning from Multiple Demonstrators., , and . IJCAI, page 2425-2431. ijcai.org, (2021)Contextual policy transfer in reinforcement learning domains via deep mixtures-of-experts., , and . UAI, volume 161 of Proceedings of Machine Learning Research, page 1787-1797. AUAI Press, (2021)Approximate and Memorize (A&M) : Settling opposing views in replay-based continuous unsupervised domain adaptation., , and . Knowl. Based Syst., (2024)Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach., , and . NeurIPS, page 9549-9559. (2018)An unsupervised spatiotemporal fusion network augmented with random mask and time-relative information modulation for anomaly detection of machines with multiple measuring points., , , and . Expert Syst. Appl., 237 (Part B): 121506 (March 2024)A two-phase preventive maintenance policy considering imperfect repair and postponed replacement., , , , and . Eur. J. Oper. Res., 274 (3): 966-977 (2019)A Marginal Log-Likelihood Approach for the Estimation of Discount Factors of Multiple Experts in Inverse Reinforcement Learning., and . IROS, page 7786-7791. IEEE, (2021)A general inspection and replacement policy for protection systems subject to shocks with state dependent effect., , and . Reliab. Eng. Syst. Saf., (2024)Domain generalization via adversarial out-domain augmentation for remaining useful life prediction of bearings under unseen conditions., , , , , and . Knowl. Based Syst., (2023)