Search Results - Multi-Task Computing

Dynamic Split Computing Framework for Multi-Task Learning Models: A Deep Reinforcement Learning Approach

Authors: Ko, Haneul Seo, Sangwon Pack, Sangheon

Source: IEEE Access.

Subject Terms: deep deterministic policy gradient, inference, multi-task learning, reinforcement learning, Split computing

File Description: print

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-366573
https://doi.org/10.1109/ACCESS.2025.3578009

DSPCI-MTL: Dynamic split point computing in multi-task learning implementation with collaborative intelligence

Authors: M.Faruk Sahin S. Faegheh Yeganli Farzad Kiani

Source: Alexandria Engineering Journal, Vol 124, Iss, Pp 404-421 (2025)

Subject Terms: IoT, Data reconstruction, Collaborative intelligence, Multi-task learning, Auto encoder, Deep neural network, TA1-2040, Engineering (General). Civil engineering (General)

Access URL: https://doaj.org/article/71fa56eaaca44aab98753ad7c69d0376

Load-Aware Multi-Task Offloading Strategy Based on Optimal Stopping Theory in Mobile Edge Computing

Authors: Chen Zhao Xiaokui Yue Mingliang Chen et al.

Source: Intelligent and Converged Networks. 6:234-246

Computing and Network Load Balancing for Decentralized Deep Federated Learning in Industrial Cyber-Physical Systems: A Multi-Task Approach

Authors: Xuening Shang Zhan Liu Deyun Gao et al.

Source: IEEE Journal on Selected Areas in Communications. 43:2997-3013

Fluid Antenna Empowered Integration of Sensing, Communications and Computing With Hybrid Multi-Task Offloading

Authors: Chenglong Dou Yuan Wu Liping Qian et al.

Source: IEEE Wireless Communications Letters. 14:2521-2525

MTL-Split: Multi-Task Learning for Edge Devices using Split Computing

Authors: Luigi Capogrosso Enrico Fraccaroli Samarjit Chakraborty et al.

Source: Proceedings of the 61st ACM/IEEE Design Automation Conference. :1-6

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing, Computer Vision and Pattern Recognition (cs.CV), Split Computing, Multi-Task Learning, Deep Neural Networks, Edge Devices, Split Computing, Multi-Task Learning, Deep Neural Networks, Edge Devices, Computer Science - Computer Vision and Pattern Recognition, Distributed, Parallel, and Cluster Computing (cs.DC), Machine Learning (cs.LG)

File Description: application/pdf

Access URL: http://arxiv.org/abs/2407.05982
https://dl.acm.org/doi/10.1145/3649329.3655686
https://doi.org/10.1145/3649329.3655686
https://hdl.handle.net/11562/1145092
https://dl.acm.org/doi/10.1145/3649329.3655686
https://doi.org/10.1145/3649329.3655686
https://hdl.handle.net/11583/2994382

A Resource Management Model for Distributed Multi-Task Applications in Fog Computing Networks

Authors: Hosseinpour, Farhoud Naebi, Ahmad Virtanen, Seppo et al.

Source: IEEE Access. 9:152792-152802

Subject Terms: Edge computing, Cloud computing, Task analysis, Resource management, Costs, Computational modeling, Delays, Greedy, fog computing, Internet of Things, modelling, optimization

File Description: print

Access URL: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-305619
https://doi.org/10.1109/ACCESS.2021.3127355

Onboard UAV State Estimation and Trajectory Prediction Using Multi-Task Reservoir Computing

Authors: Nicolas Souli Panayiotis Kardaras Yiannis Grigoriou et al.

Source: 2025 International Conference on Unmanned Aircraft Systems (ICUAS). :75-82

Energy Efficient Multi-Task Offloading in Satellite- Assisted Vehicular Edge Computing Networks: An Improved Soft-Actor-Critic Approach

Authors: Lina Wang Juan Li Minghui Dai et al.

Source: IEEE Transactions on Vehicular Technology. 74:6473-6487

A Multi-Task Supervised Compression Model for Split Computing

Authors: Matsubara, Yoshitomo Mendula, Matteo Levorato, Marco

Source: 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). :4913-4922

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Computer Science - Computer Vision and Pattern Recognition, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Image and Video Processing, Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2501.01420

Energy-Efficient Computation with DVFS using Deep Reinforcement Learning for Multi-Task Systems in Edge Computing

Authors: Xinyi Li Ti Zhou Haoyu Wang et al.

Source: IEEE Transactions on Sustainable Computing. :1-13

Subject Terms: FOS: Computer and information sciences, Computer Science - Operating Systems, Computer Science - Machine Learning, Operating Systems (cs.OS), Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2409.19434

Optimization of Multi-Task Efficient Offloading Strategy for Heterogeneous Edge Computing Based on Deep Reinforcement Learning

Authors: Sen Zhu

Source: Journal of Computer Science and Artificial Intelligence. 2:22-28

Multi-Task Wavelength-Multiplexed Reservoir Computing Using a Silicon Microring Resonator

Authors: Giron Castro, Bernard Peucheret, Christophe Zibar, Darko et al.

Contributors: Giron Castro, Bernard Peucheret, Christophe Zibar, Darko et al.

Source: 2024 International Joint Conference on Neural Networks (IJCNN). :1-7

Subject Terms: [PHYS.PHYS.PHYS-OPTICS] Physics [physics]/Physics [physics]/Optics [physics.optics], FOS: Computer and information sciences, Computer Science - Machine Learning, Emerging Technologies (cs.ET), [INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Computer Science - Neural and Evolutionary Computing, Computer Science - Emerging Technologies, FOS: Physical sciences, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], Neural and Evolutionary Computing (cs.NE), Physics - Optics, Machine Learning (cs.LG), Optics (physics.optics)

Access URL: http://arxiv.org/abs/2310.16588

Synchronous Control System Design for Embedded Multi-Task Edge Computing Based on Model Predictive Control

Authors: Bo Li Jianxin Cheng Fan Wang et al.

Source: Proceedings of the 4th International Conference on Computer, Artificial Intelligence and Control Engineering. :499-504

Multi-task classification offloading strategy based on reinforcement learning for mobile edge computing

Authors: Lei Wei Pan Wang Shaoxi Ren et al.

Source: The Journal of Supercomputing. 81

PbS Quantum Dot-Based Optoelectronic Memristors toward Multi-Task Reservoir Computing

Authors: Jiasong Lin Zhen Wang Qinghong Lin et al.

Source: The Journal of Physical Chemistry Letters. 16:199-208

Access URL: https://pubmed.ncbi.nlm.nih.gov/39714926

FedAPTA: Federated Multi-task Learning in Computing Power Networks with Adaptive Layer-wise Pruning and Task-aware Aggregation

Authors: Yuan, Yachao Yu, Zhen Wang, Jin et al.

Subject Terms: FOS: Computer and information sciences, Distributed, Parallel, and Cluster Computing, Distributed, Parallel, and Cluster Computing (cs.DC)

Access URL: http://arxiv.org/abs/2508.02230

Multi-UPF Traffic Prediction of 5G Core Network With Multi-Task Learning and Multi-core Parallel Computing

Authors: Yonghe Su Hongbing Ma Bin Chen et al.

Source: 2024 10th International Conference on Computer and Communications (ICCC). :2260-2264

Now You See Me: Embedded Mobile Edge Computing Awareness for Optimizing Multi-Task Applications in Constrained IoT Devices

Authors: Jaime Sebastian Burbano Francisco Salgado-Castillo Rolf Schuster

Source: 2024 IEEE International Conference on Smart Internet of Things (SmartIoT). :115-122

Lightweight Multi-task Hyperdimensional Computing Framework Driven by Binary Neural Network for Sleep Apnea Detection

Authors: Tian Chen Yi Liu Guanzheng Liu et al.

Source: 2024 IEEE Biomedical Circuits and Systems Conference (BioCAS). :1-5