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Cloud Computing

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/waittim/mask-detector

공식 코드

WearMask: Fast In-browser Face Mask Detection with Serverless Edge Computing for COVID-19

https://github.com/wangkai-tech23/LiPar

공식 코드

LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion Detection

https://github.com/wangziblake/MoDern

공식 코드

A Sparse Model-inspired Deep Thresholding Network for Exponential Signal Reconstruction -- Application in Fast Biological Spectroscopy

https://github.com/yashen998/gates

공식 코드

GATES: Cost-aware Dynamic Workflow Scheduling via Graph Attention Networks and Evolution Strategy

https://github.com/yuanyuan-yuan/manifold-sca

공식 코드

Automated Side Channel Analysis of Media Software with Manifold Learning

https://github.com/MLOPTPSU/FedTorch

Agnostic Federated Learning

https://github.com/MLOPTPSU/TorchFed

Agnostic Federated Learning

https://github.com/ReyhaneAskari/SLA_violation_classification

SLA Violation Prediction In Cloud Computing: A Machine Learning Perspective

https://github.com/Shuilin123/ADB

A New Interpolation Approach and Corresponding Instance-Based Learning

https://github.com/bentoml/openllm

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

https://github.com/casper-hansen/autoawq

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

https://github.com/cuis15/FCFL

Agnostic Federated Learning

https://github.com/deercoder/DeepFood

DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment

https://github.com/efficient-large-model/vila

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

https://github.com/fairfl/FCFL

Agnostic Federated Learning

https://github.com/fairfl/FUEL

Agnostic Federated Learning

https://github.com/hennas/eisim

Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration

https://github.com/hfp/libxsmm

Optimizing Deep Learning Recommender Systems' Training On CPU Cluster Architectures

https://github.com/huaweicloud/vm-placement-dataset

VMAgent: Scheduling Simulator for Reinforcement Learning

https://github.com/kishordgupta/pyod

ADGym: Design Choices for Deep Anomaly Detection

https://github.com/libxsmm/libxsmm

Optimizing Deep Learning Recommender Systems' Training On CPU Cluster Architectures

https://github.com/litian96/fair_flearn

Agnostic Federated Learning

https://github.com/lucashu1/land-cover

SEN12MS -- A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion

https://github.com/mibarg/IP-Grouping

Learning to Customize Network Security Rules

https://github.com/nvlabs/vila

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

https://github.com/qwenlm/qwen2-vl

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

https://github.com/rasmith/vllm

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

https://github.com/sheetalreddy/Three-Approaches-for-Personalization-with-Applications-to-FederatedLearning

Three Approaches for Personalization with Applications to Federated Learning

https://github.com/shengchaochen82/metepfl

Federated Prompt Learning for Weather Foundation Models on Devices

https://github.com/umitkacar/ai-edge-computing

Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

https://github.com/vaseline555/aaggff

Agnostic Federated Learning

https://github.com/vectorch-ai/scalellm

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

https://github.com/xinzhel/kaggle-toxicity-2021

Convolutional Neural Networks for Toxic Comment Classification

https://github.com/xvyaward/qeft

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

https://github.com/yuchuang1205/rrl-net

SEN12MS -- A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion

https://github.com/yzhao062/pyod

ADGym: Design Choices for Deep Anomaly Detection

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.