Multitask Learning Over Graphs: An Approach for Distributed, Streaming Machine Learning

Citations Over TimeTop 10% of 2020 papers

Abstract

The problem of simultaneously learning several related tasks has received considerable attention in several domains, especially in machine learning, with the so-called multitask learning (MTL) problem, or learning to learn problem [1], [2]. MTL is an approach to inductive transfer learning (using what is learned for one problem to assist with another problem), and it helps improve generalization performance relative to learning each task separately by using the domain information contained in the training signals of related tasks as an inductive bias. Several strategies have been derived within this community under the assumption that all data are available beforehand at a fusion center.

Related Papers

• → Evaluation of Transfer Learning Algorithms Using Different Base Learners(2017)6 cited
• → Machine Learning(2020)1 cited
• → Inductive learning: Algorithms and frontiers(1993)13 cited
• → Introduction to Machine Learning(2019)5 cited
• → Multi-Inductive Learning approach for Information Extraction(2011)