Multitask learning over shared subspaces. [PDF]
PLoS Computational Biology, 2021 This paper uses constructs from machine learning to define pairs of learning tasks that either shared or did not share a common subspace. Human subjects then learnt these tasks using a feedback-based approach and we hypothesised that learning would be ...
Nicholas Menghi, Kemal Kacar, Will Penny
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A novel multitask learning algorithm for tasks with distinct chemical space: zebrafish toxicity prediction as an example [PDF]
Journal of CheminformaticsData scarcity is one of the most critical issues impeding the development of prediction models for chemical effects. Multitask learning algorithms leveraging knowledge from relevant tasks showed potential for dealing with tasks with limited data. However,
Run-Hsin Lin +3 more
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Development of a diagnostic classification model for lateral cephalograms based on multitask learning [PDF]
BMC Oral HealthObjectives This study aimed to develop a cephalometric classification method based on multitask learning for eight diagnostic classifications. Methods This study was retrospective.
Qiao Chang +7 more
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Multitask Learning for Mental Health: Depression, Anxiety, Stress (DAS) Using Wearables [PDF]
DiagnosticsThis study investigates the prediction of mental well-being factors—depression, stress, and anxiety—using the NetHealth dataset from college students. The research addresses four key questions, exploring the impact of digital biomarkers on these factors,
Berrenur Saylam, Özlem Durmaz İncel
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Multitask learning to predict successful weaning in critically ill ventilated patients: A retrospective analysis of the MIMIC-IV database [PDF]
Digital HealthObjective Weaning is an essential issue in critical care. This study explores the efficacy of multitask learning models in predicting successful weaning in critically ill ventilated patients using the Medical Information Mart for Intensive Care (MIMIC ...
Ming-Yen Lin +2 more
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Trainable Weights for Multitask Learning
IEEE Access, 2023 The research on multi-task learning has been steadily increasing due to its advantages, such as preventing overfitting, averting catastrophic forgetting, solving multiple inseparable tasks, and coping with data shortage.
Chaeeun Ryu +6 more
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A Multiscale and Multitask Deep Learning Framework for Automatic Building Extraction
Remote Sensing, 2022 Detecting buildings, segmenting building footprints, and extracting building edges from high-resolution remote sensing images are vital in applications such as urban planning, change detection, smart cities, and map-making and updating.
Jichong Yin +5 more
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Speech Emotion and Naturalness Recognitions With Multitask and Single-Task Learnings
IEEE Access, 2022 This paper evaluates speech emotion and naturalness recognitions by utilizing deep learning models with multitask learning and single-task learning approaches. The emotion model accommodates valence, arousal, and dominance attributes known as dimensional
Bagus Tris Atmaja +2 more
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Stacker Scheduling and Repository Location Recommendation Based on Multi-Task Reinforcement Learning [PDF]
Jisuanji gongcheng, 2023 Stacker scheduling is an essential task in warehousing automation.Inbound-outbound efficiency and storage situations affect overall efficiency.When handling large-scale problems, traditional scheduling methods cannot achieve performance because ...
RAO Dongning, LUO Nanyue
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Clustering by Errors: A Self-Organized Multitask Learning Method for Acoustic Scene Classification
Sensors, 2021 Acoustic scene classification (ASC) tries to inference information about the environment using audio segments. The inter-class similarity is a significant issue in ASC as acoustic scenes with different labels may sound quite similar.
Weiping Zheng, Zhenyao Mo, Gansen Zhao
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