Papers with Code 아카이브
Edge-computing
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 0
- 보존된 평가 기록
- 0
Deep Learning on EDGE devices
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mmalekzadeh/vicious-classifiers
공식 코드Vicious Classifiers: Assessing Inference-time Data Reconstruction Risk in Edge Computing
https://github.com/mostafaelhoushi/DeepShift
공식 코드https://github.com/mxahan/rPPG_edge_implementation
공식 코드Demo: RhythmEdge: Enabling Contactless Heart Rate Estimation on the Edge
https://github.com/naram-m/online-caching
공식 코드https://github.com/neufieldrobotics/neuflow
공식 코드NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots Using Edge Devices
https://github.com/nmi-lab/snn_maml
공식 코드Emulating Brain-like Rapid Learning in Neuromorphic Edge Computing
https://github.com/noahzn/ddnd
공식 코드Channel-Aware Distillation Transformer for Depth Estimation on Nano Drones
https://github.com/ofontenla/fedheonn
공식 코드An effective and efficient green federated learning method for one-layer neural networks
https://github.com/omnia-unist/Miro
공식 코드Cost-effective On-device Continual Learning over Memory Hierarchy with Miro
https://github.com/open-digital-twin/framework-fl-testing
공식 코드A Framework for testing Federated Learning algorithms using an edge-like environment
https://github.com/princetonlips/mf-bocd
공식 코드https://github.com/publioelon/leaf-diseases-classification
공식 코드An Edge Computing-Based Solution for Real-Time Leaf Disease Classification using Thermal Imaging
https://github.com/pulp-platform/dory
공식 코드DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs
https://github.com/pulp-platform/fann-on-mcu
공식 코드https://github.com/qiongwu86/Cooperative-Caching-in-VEC-Based-on-Asynchronous-FL-and-DRL
공식 코드https://github.com/qiongwu86/DDPG-RIS-MADDPG-POWER
공식 코드https://github.com/qiongwu86/digital-twin-vehicular-edge-computing-network_task-offloading-and-resource-allocation
공식 코드Digital Twin Vehicular Edge Computing Network: Task Offloading and Resource Allocation
https://github.com/qiongwu86/federated-ssl-task-offloading-and-resource-allocation
공식 코드https://github.com/qiongwu86/optimizing-aoi-in-vec-with-federated-graph-neural-network-multi-agent-reinforcement-learning
공식 코드https://github.com/qiongwu86/resource-allocation-for-twin-maintenance-and-computing-tasks-in-digital-twin-mobile-edge-network
공식 코드https://github.com/qiongwu86/ris-vec-marl
공식 코드Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learning
https://github.com/qiyu3816/MTFNN-CO
공식 코드A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading
https://github.com/qlt315/blockchain-enabled-ucmec
공식 코드Energy-Efficient Blockchain-enabled User-Centric Mobile Edge Computing
https://github.com/qlt315/ucmec-mmwave-fronthaul
공식 코드Towards Decentralized Task Offloading and Resource Allocation in User-Centric Mobile Edge Computing
https://github.com/qub-blesson/AdaptiveDNN
공식 코드https://github.com/racsa-lab/EDD
공식 코드Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural Network
https://github.com/restuccia-group/entropyr
공식 코드https://github.com/rezafuru/frankensplit
공식 코드https://github.com/rezafuru/quantensplit
공식 코드Architectural Vision for Quantum Computing in the Edge-Cloud Continuum
https://github.com/rezafuru/the-fool
공식 코드FOOL: Addressing the Downlink Bottleneck in Satellite Computing with Neural Feature Compression
https://github.com/rmazzier/OpenSetGaitRecognition_PCAA
공식 코드Open-Set Gait Recognition from Sparse mmWave Radar Point Clouds
https://github.com/robobachelor/keyword-spotting-stm32
공식 코드https://github.com/romoreira/sfi2-energy-sustainability
공식 코드https://github.com/sagnik106/clustered-fl-ga
공식 코드Genetic CFL: Optimization of Hyper-Parameters in Clustered Federated Learning
https://github.com/sahelyiyi/FederatedLearning
공식 코드Clustered Federated Learning via Generalized Total Variation Minimization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.