Results 51 to 60 of about 603,409 (311)
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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Multi-population genomic prediction using a multi-task Bayesian learning model
, 2014 Background Genomic prediction in multiple populations can be viewed as a multi-task learning problem where tasks are to derive prediction equations for each population and multi-task learning property can be improved by sharing information across ...
Li, C. +3 more
core +1 more source
Towards Impartial Multi-task Learning [PDF]
, 2021 Multi-task learning (MTL) has been widely used in representation learning. However, naively training all tasks simultaneously may lead to the partial training issue, where specific tasks are trained more adequately than others.
Kuang, Z +7 more
core
Design and analysis strategies for robust microbiome ageing research
FEBS Letters, EarlyView.The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik +5 more
wiley +1 more source
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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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 dynamical systems: customising time series models [PDF]
, 2021 Time series datasets are usually composed of a variety of sequences from the same domain, but from different entities, such as individuals, products, or organizations.
Bird, Alex
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Interpreting the effects of DNA polymerase variants at the structural level
Molecular Oncology, EarlyView.Using MAVISp and molecular dynamics simulations, we analyzed over 60 000 missense variants in POLE and POLD1 from ClinVar, COSMIC, cBioPortal, and saturation mutagenesis. Identified mechanistic indicators, including stability, binding, and long‐range, enable structural interpretation, providing ACMG‐like evidence for possible reclassification of VUS ...
Matteo Arnaudi +7 more
wiley +1 more source
Uniform Loss Versus Specialized Optimization: A Comparative Analysis in Multi-Task Learning
IEEE AccessSpecialized Multi-Task Optimizers (SMTOs) balance task learning in Multi-Task Learning by addressing issues like conflicting gradients and differing gradient norms, which hinder equal-weighted task training. However, recent critiques suggest that equally
Gabriel S. Gama, Valdir Grassi
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Network Clustering for Multi-task Learning
CoRR, 2021 The Multi-Task Learning (MTL) technique has been widely studied by word-wide researchers. The majority of current MTL studies adopt the hard parameter sharing structure, where hard layers tend to learn general representations over all tasks and specific layers are prone to learn specific representations for each task. Since the specific layers directly
Dehong Gao +5 more
openaire +2 more sources