Efficient multi-task feature learning with calibration

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Multi-Task Feature Interaction Learning (2016, background): “Such problems are especially prevalent in biological and medical domains, where MTL has achieved signiﬁcant success, including data analysis on genotype and gene expression [21], breast cancer diagnosis [37] and progression modeling of Alzheimer’s Disease [18], etc.”
Interactive Multi-task Relationship Learning (2016, background): “MTL has many successful applications in board fields such as data mining, computer vision, text mining, bioinformatics and healthcare analytics [22, 33, 16].”
Amalgamating Knowledge from Heterogeneous Graph Neural Networks (2021, background): “Multi-task learning aims to leverage task relatedness to jointly learn a group of tasks with shared architectures [1, 4, 5, 8, 17, 50].”
Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition (2017, background): “Multi-task learning has been widely studied in machine learning [3], [15] and computer vision [51], [63].”
Data-Free Knowledge Amalgamation via Group-Stack Dual-GAN (2020, background): “Multi-task learning(MTL) has been widely studied in many fields, including computer vision [8, 1], natural language processing [4], and machine learning [26, 41].”
Multi-task deep visual-semantic embedding for video thumbnail selection (2015, background): “Thus, multi-task learning is especially beneficial when the training sample size is small for each task [3].”