Autor:	Petangoda, Janith, Monk, Nick A. M., Deisenroth, Marc Peter
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Transfer learning considers a learning process where a new task is solved by transferring relevant knowledge from known solutions to related tasks. While this has been studied experimentally, there lacks a foundational description of the transfer learning problem that exposes what related tasks are, and how they can be exploited. In this work, we present a definition for relatedness between tasks and identify foliations as a mathematical framework to represent such relationships. Comment: 14 pages, 6 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2008.00546 Zobrazit plný text záznamu View this record from Arxiv