Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence Learning High-Order Task Relationships in Multi-Task Learning

By Yu Zhang, Dit-Yan Yeung. 2013

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Citations in the corpus

An Overview of Multi-task Learning (2018, methodology): “Since the prior used in the MTRL method implies that W T W follows a Wishart distribution as W ( 0 , (cid:6) ), the MTRL method is generalized in [50] by studying a high-order prior: ( W T W ) t ∼ W ( 0 , (cid:6) ), where t is a positive integer.”
A Survey on Multi-Task Learning (2017, methodology): “(20) implies that WTW follows a Wishart distribution asW(·|0,Ω), Zhang and Yeung [91] generalize it as (WTW)t ∼ W(·|0,Ω), (22) where t is a positive integer to model high-order task relationships.”
Multi-Task Learning and Algorithmic Stability (2015, methodology): “…al. 2007), learning the task relation from data in different ways (Jacob, Bach, and Vert 2008; Zhang and Yeung 2010a; Solnon, Arlot, and Bach 2012; Zhang and Yeung 2013; 2014), decomposition methods (Chen, Liu, and Ye 2010; Jalali et al. 2010; Lozano and Swirszcz 2012; Zweig and Weinshall 2013)…”
Flexible Clustered Multi-Task Learning by Learning Representative Tasks (2016, methodology): “Several previous works also studied the non-linear extension of MTL methods [3], [16], [20], [30], [45], [55], [57].”
Action2Activity: Recognizing Complex Activities from Sensor Data (2015, methodology): “Bayesian network-based approaches [ Zhang et al. , 2013 ] are also able to model the temporal relations among actions.”
Learning to Multitask (2018, background): “This approach can be further classified into four categories: low-rank approach [1, 28, 17], task clustering approach [18, 20, 15], task relation learning approach [39, 40, 36, 35, 41, 42, 21, 37], and decomposition approach [10, 19, 44, 16].”