https://github.com/hupihe/rsd4
공식 코드Effective Multi-User Delay-Constrained Scheduling with Deep Recurrent Reinforcement Learning
Papers with Code 아카이브
Cloud Computing
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/idaslab/intec_framework
공식 코드https://github.com/imperial-qore/COSCO
공식 코드HUNTER: AI based Holistic Resource Management for Sustainable Cloud Computing
https://github.com/imperial-qore/cilp
공식 코드https://github.com/imperial-qore/metanet
공식 코드MetaNet: Automated Dynamic Selection of Scheduling Policies in Cloud Environments
https://github.com/internlm/lmdeploy
공식 코드AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
https://github.com/juan-esteban-berger/options_pricing_automl_tensorflow_xgboost
공식 코드Pricing European Options with Google AutoML, TensorFlow, and XGBoost
https://github.com/jubueche/On-Chip-EBN-Learning
공식 코드Implementing efficient balanced networks with mixed-signal spike-based learning circuits
https://github.com/kiguli/impact
공식 코드https://github.com/lgrativol/fl_exps
공식 코드Federated learning compression designed for lightweight communications
https://github.com/linkpark/pomdp-service-migration
공식 코드https://github.com/luoxi-model/luoxi_models
공식 코드Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI
https://github.com/mail-ecnu/vmagent
공식 코드https://github.com/miirho3ein/real_time_bhm
공식 코드Exploring Scalable, Distributed Real-Time Anomaly Detection for Bridge Health Monitoring
https://github.com/minqi824/adgym
공식 코드https://github.com/mit-han-lab/llm-awq
공식 코드AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
https://github.com/mkhaled87/pFaces-AMYTISS
공식 코드AMYTISS: Parallelized Automated Controller Synthesis for Large-Scale Stochastic Systems
https://github.com/mmv-lab/data-compression
공식 코드Deep learning based Image Compression for Microscopy Images: An Empirical Study
https://github.com/nets4geeks/dockercomposedataset
공식 코드https://github.com/neurodata/sic
공식 코드https://github.com/nvidia/tensorrt-llm
공식 코드AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
https://github.com/onlinehuazai/evit
공식 코드EViT: Privacy-Preserving Image Retrieval via Encrypted Vision Transformer in Cloud Computing
https://github.com/pacslab/conc-value-perf-modelling
공식 코드Performance Modeling of Metric-Based Serverless Computing Platforms
https://github.com/rayankrish/CpuUsagePrediction
공식 코드https://github.com/remis/mining-discovery-with-deep-learning
공식 코드Mining and Tailings Dam Detection In Satellite Imagery Using Deep Learning
https://github.com/romoreira/sfi2-energy-sustainability
공식 코드https://github.com/scale-lab/EDAonCloud
공식 코드https://github.com/sharmapulkit/TreeInterpretability_AnomalyExplanation
공식 코드https://github.com/shengchaochen82/fedpod
공식 코드Federated Prompt Learning for Weather Foundation Models on Devices
https://github.com/signaturescience/fiphde
공식 코드https://github.com/su-cisec/mlwithhe
공식 코드ML with HE: Privacy Preserving Machine Learning Inferences for Genome Studies
https://github.com/sunkairan/MapReduce-Based-Deep-Learning
공식 코드Large-scale Artificial Neural Network: MapReduce-based Deep Learning
https://github.com/tongnie/tensorlib
공식 코드https://github.com/uf-aiaos/shinyanimalcv
공식 코드https://github.com/uniqzheng/hfr-bvqa
공식 코드FAVER: Blind Quality Prediction of Variable Frame Rate Videos
https://github.com/vllm-project/vllm
공식 코드AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.