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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 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
doaj +8 more sources
Machine Learning, 1997 Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared representation; what is learned for each task can help other tasks be learned better.
R. Caruana
openaire +2 more sources
Bayesian multitask inverse reinforcement learning [PDF]
, 2011 We generalise the problem of inverse reinforcement learning to multiple tasks, from multiple demonstrations. Each one may represent one expert trying to solve a different task, or as different experts trying to solve the same task.
C.A. Rothkopf +3 more
core +6 more sources
Optimal Schedules in Multitask Motor Learning [PDF]
Neural Computation, 2016 Although scheduling multiple tasks in motor learning to maximize long-term retention of performance is of great practical importance in sports training and motor rehabilitation after brain injury, it is unclear how to do so.
Kim, Sung Shin +4 more
core +5 more sources
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
doaj +2 more sources
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
doaj +2 more sources
Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU [PDF]
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018 Machine learning approaches have been effective in predicting adverse outcomes in different clinical settings. These models are often developed and evaluated on datasets with heterogeneous patient populations.
Buolamwini Joy +6 more
core +2 more sources
The Benefit of Multitask Representation Learning [PDF]
, 2016 We discuss a general method to learn data representations from multiple tasks. We provide a justification for this method in both settings of multitask learning and learning-to-learn.
Maurer, Andreas +2 more
core +4 more sources
Pushing the Boundaries of Molecular Property Prediction for Drug Discovery with Multitask Learning BERT Enhanced by SMILES Enumeration. [PDF]
Research (Wash D C), 2022 Accurate prediction of pharmacological properties of small molecules is becoming increasingly important in drug discovery. Traditional feature-engineering approaches heavily rely on handcrafted descriptors and/or fingerprints, which need extensive human ...
Zhang XC +7 more
europepmc +2 more sources