Multi-Task Learning for Spatio-Temporal Event Forecasting

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An Overview of Multi-task Learning (2018, neutral): “…tagging, chunking, named entity recognition, semantic role labeling, language modeling and semantically related words) [116], multi-domain sentiment classification [117], multi-domain dialog state tracking [21], machine translation [118], syntactic parsing [118], and microblog analysis [119,120].”
A Survey on Multi-Task Learning (2017, background): “Feature Selection Face and object recognition [211] siRNA efficacy prediction [212] Microblog analysis [119] Behavioral targeting [213] Brain imaging [214] Genetic marker detection [215] Mental state examination [216] Predict cognitive outcome [217] Survival analysis [120] Genetic trait prediction [”
Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta Learning (2019, background): “Another group of works [16, 23, 28] adopt multi-task learning approaches, which mainly build multiple sub-models for each location and all of these sub-models are trained together by using similarity constraints between locations.”
Spatio-Temporal Data Mining (2017, methodology): “While early efforts in using tensor-based factorization [Takahashi et al. 2017], multi-task learning [Zhao et al. 2015], and spatio-temporal kernel density estimation [Zhou and Matteson 2015] approaches have shown promise, their effectiveness in handling heterogeneity and sparsity is yet to be…”