Results 11 to 20 of about 3,537 (211)

Development of a multi-task learning framework with gradnorm for precise wound tissue analysis. [PDF]

PLoS ONE
Chronic wounds impose a substantial burden on patients and healthcare systems, necessitating accurate qualification of precise wound analysis for effective diagnosis and treatment.
Hyunyoung Kang, Byungho Oh, Solam Lee, YuSung Chu, Jiye Kim, Sejung Yang   +5 more
doaj   +2 more sources

A multi-task learning approach combining regression and classification tasks for joint feature selection [PDF]

Scientific Reports
Multi-task learning (MTL) is a learning paradigm that enables the simultaneous training of multiple communicating algorithms, and has been widely applied in the biomedical analysis for shared biomarker identification.
Han Cao, Sivanesan Rajan, Bianka Hahn, Ersoy Kocak, Manuel Brenner, Florian Hess, Roman Schefzik, Daniel Durstewitz, Georgia Koppe, Emanuel Schwarz, Verena Schneider-Lindner   +10 more
doaj   +2 more sources

Direct Routing Gradient (DRGrad): A Personalized Information Surgery for Multi-Task Learning (MTL) Recommendations

Proceedings of the AAAI Conference on Artificial Intelligence
Multi-task learning (MTL) has emerged as a successful strategy in industrial-scale recommender systems, offering significant advantages such as capturing diverse users’ interests and accurately detecting different behaviors like “click" or “dwell time".
Yuguang Liu, Yiyun Miao, Luyao Xia
openaire   +3 more sources

CMI-MTL: Cross-Mamba interaction based multi-task learning for medical visual question answering

CoRR
Medical visual question answering (Med-VQA) is a crucial multimodal task in clinical decision support and telemedicine. Recent self-attention based methods struggle to effectively handle cross-modal semantic alignments between vision and language. Moreover, classification-based methods rely on predefined answer sets.
Qiangguo Jin, Xianyao Zheng, Hui Cui 0002, Changming Sun, Yuqi Fang, Cong Cong 0001, Ran Su, Leyi Wei, Ping Xuan, Junbo Wang   +9 more
openaire   +3 more sources

Multi-task meta-initialized DQN for fast adaptation to unseen slicing tasks in O-RAN. [PDF]

PLoS ONE
The open radio access network (O-RAN) architecture facilitates intelligent radio resource management via RAN intelligent controllers (RICs). Deep reinforcement learning (DRL) algorithms are integrated into RICs to address dynamic O-RAN slicing challenges.
Bosen Zeng, Xianhua Niu
doaj   +2 more sources

Modeling and application of alzheimer’s disease complex trait prediction based on multi-task learning [PDF]

Scientific Reports
Alzheimer’s disease (AD) is a complex disorder influenced by genetic factors, and related phenotypes often share common genetic mechanisms. However, traditional genetic risk prediction models typically focus on a single phenotype, neglecting valuable ...
Wenchao Zhou, Zhao Xue, Jiaqi Liang, Jing Cui, Xiangjie Guo, Yalu Wen   +5 more
doaj   +2 more sources

Multi-State Online Estimation of Lithium-Ion Batteries Based on Multi-Task Learning

Energies, 2023
Deep learning-based state estimation of lithium batteries is widely used in battery management system (BMS) design. However, due to the limitation of on-board computing resources, multiple single-state estimation models are more difficult to deploy in ...
Xiang Bao, Yuefeng Liu, Bo Liu, Haofeng Liu, Yue Wang   +4 more
doaj   +1 more source

A safe reinforcement learning approach for autonomous navigation of mobile robots in dynamic environments

CAAI Transactions on Intelligence Technology, EarlyView., 2023
Abstract When deploying mobile robots in real‐world scenarios, such as airports, train stations, hospitals, and schools, collisions with pedestrians are intolerable and catastrophic. Motion safety becomes one of the most fundamental requirements for mobile robots.
Zhiqian Zhou, Junkai Ren, Zhiwen Zeng, Junhao Xiao, Xinglong Zhang, Xian Guo, Zongtan Zhou, Huimin Lu   +7 more
wiley   +1 more source

Advancing translational research in neuroscience through multi-task learning

Frontiers in Psychiatry, 2022
Translational research in neuroscience is increasingly focusing on the analysis of multi-modal data, in order to account for the biological complexity of suspected disease mechanisms.
Han Cao, Xudong Hong, Xudong Hong, Heike Tost, Andreas Meyer-Lindenberg, Emanuel Schwarz   +5 more
doaj   +1 more source

Adaptive Dynamic Search for Multi-Task Learning

Applied Sciences, 2022
Multi-task learning (MTL) is a learning strategy for solving multiple tasks simultaneously while exploiting commonalities and differences between tasks for improved learning efficiency and prediction performance.
Eunwoo Kim
doaj   +1 more source