SOTAVerified

Edge-computing

Deep Learning on EDGE devices

Papers

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Showing 1076–1100 of 1279 papers

TitleStatusHype
Reconfigurable Intelligent Surface for Sensing, Communication, and Computation: Perspectives, Challenges, and Opportunities—0
Multi-Agent Task Assignment in Vehicular Edge Computing: A Regret-Matching Learning-Based Approach—0
Reinforcement Learning-based Dynamic Service Placement in Vehicular Networks—0
Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence—0
Reinforcement Learning for Adaptive Resource Scheduling in Complex System Environments—0
Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing—0
Reliable Fleet Analytics for Edge IoT Solutions—0
Representation Similarity: A Better Guidance of DNN Layer Sharing for Edge Computing without Training—0
Research on Edge Computing and Cloud Collaborative Resource Scheduling Optimization Based on Deep Reinforcement Learning—0
Research on Resource Allocation for Efficient Federated Learning—0
Resolution Strategies for Networking the IoT at the Edge via Named Functions—0
ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification—0
Resource allocation algorithm for MEC based on Deep Reinforcement Learning—0
Resource Allocation for Intelligent Reflecting Surface Aided Wireless Powered Mobile Edge Computing in OFDM Systems—0
Resource Allocation for Semantic-Aware Mobile Edge Computing Systems—0
Resource Allocation for Stable LLM Training in Mobile Edge Computing—0
Rethinking Deconvolution for 2D Human Pose Estimation Light yet Accurate Model for Real-time Edge Computing—0
Retrieval-Augmented Generation for Mobile Edge Computing via Large Language Model—0
Revenue and Energy Efficiency-Driven Delay Constrained Computing Task Offloading and Resource Allocation in a Vehicular Edge Computing Network: A Deep Reinforcement Learning Approach—0
RIS-Assisted UAV for Timely Data Collection in IoT Networks—0
Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A Multi-Agent Deep Reinforcement Learning Approach—0
Roadmap for Edge AI: A Dagstuhl Perspective—0
Robust Computation Offloading and Trajectory Optimization for Multi-UAV-Assisted MEC: A Multi-Agent DRL Approach—0
Robust Decentralized Learning with Local Updates and Gradient Tracking—0
Robustness of Neural Networks to Parameter Quantization—0
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