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Edge-computing
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Deep Learning on EDGE devices
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hwang52/fedskc
공식 코드FedSKC: Federated Learning with Non-IID Data via Structural Knowledge Collaboration
https://github.com/i3tyc/adaptslam
공식 코드AdaptSLAM: Edge-Assisted Adaptive SLAM with Resource Constraints via Uncertainty Minimization
https://github.com/ichakra2/pca-hybrid
공식 코드https://github.com/icolbert/upsampling
공식 코드An Energy-Efficient Edge Computing Paradigm for Convolution-based Image Upsampling
https://github.com/idaslab/intec_framework
공식 코드https://github.com/idea-research/grounding-dino-1.5-api
공식 코드Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection
https://github.com/imperial-qore/COSCO
공식 코드MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems
https://github.com/imperial-qore/pregan
공식 코드PreGAN: Preemptive Migration Prediction Network for Proactive Fault-Tolerant Edge Computing
https://github.com/imperial-qore/splitplace
공식 코드https://github.com/jalalilabucla/phycv
공식 코드https://github.com/jennamk14/adae_model
공식 코드Characterizing and Modeling AI-Driven Animal Ecology Studies at the Edge
https://github.com/jiahuiiiiii/adapi
공식 코드AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing
https://github.com/jiaqian/robustness_of_FL
공식 코드Robustness analytics to data heterogeneity in edge computing
https://github.com/jjzgeeks/FL-DLT3
공식 코드https://github.com/jubueche/On-Chip-EBN-Learning
공식 코드Implementing efficient balanced networks with mixed-signal spike-based learning circuits
https://github.com/jyzgh/dapperfl
공식 코드DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices
https://github.com/jyzgh/fedlps
공식 코드FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing
https://github.com/kfoysalhaque/sawec
공식 코드https://github.com/lancelotxwx/swift
공식 코드SWIFT: Mapping Sub-series with Wavelet Decomposition Improves Time Series Forecasting
https://github.com/lanl/spikingBackprop
공식 코드The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware
https://github.com/ldxdu/fedfusion
공식 코드FedFusion: Manifold Driven Federated Learning for Multi-satellite and Multi-modality Fusion
https://github.com/lijiannuist/efficient-multimodal-llms-survey
공식 코드https://github.com/linkpark/metarl-offloading
공식 코드Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learning
https://github.com/linkpark/pomdp-service-migration
공식 코드https://github.com/lsh9832/edgeyolo
공식 코드https://github.com/lucaballotta/processingnetworks-rl
공식 코드To Compute or not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing
https://github.com/lucaballotta/vrem-fl
공식 코드VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning
https://github.com/luoxi-model/luoxi_models
공식 코드Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI
https://github.com/lx10077/fedavgpy
공식 코드https://github.com/maftej/rfdvc
공식 코드Radiance Field Delta Video Compression in Edge-Enabled Vehicular Metaverse
https://github.com/mahmoudkanazzal/semidecentralizedhetgnnlstm
공식 코드https://github.com/malikaratnayake/polytrack2.0
공식 코드Spatial Monitoring and Insect Behavioural Analysis Using Computer Vision for Precision Pollination
https://github.com/martyrpenink/sdpose
공식 코드SDPose: Tokenized Pose Estimation via Circulation-Guide Self-Distillation
https://github.com/masterhow/eventpointpose
공식 코드https://github.com/matthewszhang/fastcan
공식 코드Canonical-Correlation-Based Fast Feature Selection for Structural Health Monitoring
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.