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Bayesian transfer learning between autoregressive inference tasks
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SYSNO ASEP 0538247 Document Type V - Research Report R&D Document Type The record was not marked in the RIV Title Bayesian transfer learning between autoregressive inference tasks Author(s) Barber, Alec (UTIA-B)
Quinn, Anthony (UTIA-B) ORCIDNumber of authors 2 Issue data Praha: ÚTIA AV ČR, 2020 Series Research Report Series number 2389 Publication form Print - P Language eng - English Country CZ - Czech Republic Keywords autoregression ; transfer learning ; Fully Probabilistic Design ; FPD ; food-commodities price prediction Subject RIV BD - Theory of Information OECD category Applied mathematics R&D Projects GA18-15970S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 Annotation Bayesian transfer learning typically relies on a complete stochastic dependence speci cation between source and target learners which allows the opportunity for Bayesian conditioning. We advocate that any requirement for the design or assumption of a full model between target and sources is a restrictive form of transfer learning. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2021
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