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Multi-Task Learning with Summary Statistics
Advances in neural information processing systems, 2023 Multi-task learning has emerged as a powerful machine learning paradigm for integrating data from multiple sources, leveraging similarities between tasks to improve overall model performance. However, the application of multi-task learning to real-world settings is hindered by data-sharing constraints, especially in healthcare settings. To address this
Knight, Parker, Duan, Rui
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Multi-task learning to leverage partially annotated data for PPI interface prediction
Scientific Reports, 2022 Protein protein interactions (PPI) are crucial for protein functioning, nevertheless predicting residues in PPI interfaces from the protein sequence remains a challenging problem.
Henriette Capel +2 more
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Multi-task Self-Supervised Visual Learning [PDF]
2017 IEEE International Conference on Computer Vision (ICCV), 2017 Published at ICCV ...
Doersch, C, Zisserman, A
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Survey of Multi-task Recommendation Algorithms [PDF]
Jisuanji kexue yu tansuoSingle-task recommendation algorithms have problems such as sparse data, cold start and unstable recommendation effect. Multi-task recommendation algorithms can jointly model multiple types of user behaviour data and additional information, to better ...
WEN Minwei, MEI Hongyan, YUAN Fengyuan, ZHANG Xiaoyu, ZHANG Xing
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ConnectomeNet: A Unified Deep Neural Network Modeling Framework for Multi-Task Learning
IEEE Access, 2023 Despite recent advances in deep neural networks (DNNs), multi-task learning has not been able to utilize DNNs thoroughly. The current method of DNN design for a single task requires considerable skill in deciding many architecture parameters a priori ...
Heechul Lim, Kang-Wook Chon, Min-Soo Kim
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Learning Task Relatedness in Multi-Task Learning for Images in Context
, 2019 Multimedia applications often require concurrent solutions to multiple tasks. These tasks hold clues to each-others solutions, however as these relations can be complex this remains a rarely utilized property.
Hal Daumé +7 more
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Multi‐station multi‐robot task assignment method based on deep reinforcement learning
CAAI Transactions on Intelligence TechnologyThis paper focuses on the problem of multi‐station multi‐robot spot welding task assignment, and proposes a deep reinforcement learning (DRL) framework, which is made up of a public graph attention network and independent policy networks.
Junnan Zhang, Ke Wang, Chaoxu Mu
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Gut microbiome and aging—A dynamic interplay of microbes, metabolites, and the immune system
FEBS Letters, EarlyView.Age‐dependent shifts in microbial communities engender shifts in microbial metabolite profiles. These in turn drive shifts in barrier surface permeability of the gut and brain and induce immune activation. When paired with preexisting age‐related chronic inflammation this increases the risk of neuroinflammation and neurodegenerative diseases.
Aaron Mehl, Eran Blacher
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Applied Sciences, 2021 In open-ended continuous environments, robots need to learn multiple parameterised control tasks in hierarchical reinforcement learning. We hypothesise that the most complex tasks can be learned more easily by transferring knowledge from simpler tasks ...
Nicolas Duminy +4 more
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Distributed Multi-Task Relationship Learning
, 2017 Multi-task learning aims to learn multiple tasks jointly by exploiting their relatedness to improve the generalization performance for each task. Traditionally, to perform multi-task learning, one needs to centralize data from all the tasks to a single ...
Ho, Qirong +2 more
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