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Scheduling

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

https://github.com/MikeTimmerman-ae/RL_Drone_Control

공식 코드

Adaptive Gain Scheduling using Reinforcement Learning for Quadcopter Control

https://github.com/NVlabs/GA3C

공식 코드

Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU

https://github.com/OpenLLMAI/OpenRLHF

공식 코드

OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework

https://github.com/OrangeSodahub/CRLFnet

공식 코드

RMMDet: Road-Side Multitype and Multigroup Sensor Detection System for Autonomous Driving

https://github.com/PaddlePaddle/Paddle

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HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments

https://github.com/Palantir-SR/palantir

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Palantir: Towards Efficient Super Resolution for Ultra-high-definition Live Streaming

https://github.com/QianrenLi/ReinWiFi

공식 코드

ReinWiFi: Application-Layer QoS Optimization of WiFi Networks with Reinforcement Learning

https://github.com/QingchuanLyu/Genetic-Algorithm-with-Border-Trades

공식 코드

Genetic Algorithm with Innovative Chromosome Patterns in the Breeding Process

https://github.com/Rainlabuw/rl-enabled-distributed-assignment

공식 코드

Multi Agent Reinforcement Learning for Sequential Satellite Assignment Problems

https://github.com/RiS3-Lab/muse

공식 코드

MEUZZ: Smart Seed Scheduling for Hybrid Fuzzing

https://github.com/RonLevie/RadioUNet

공식 코드

RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networks

https://github.com/RostyslavUA/fdTrainGNN

공식 코드

Fully Distributed Online Training of Graph Neural Networks in Networked Systems

https://github.com/SDM-TIB/SDM-RDFizer

공식 코드

Scaling Up Knowledge Graph Creation to Large and Heterogeneous Data Sources

https://github.com/SEU-WENJIA/ST-SepNet-Lightweight-LLMs-Meet-Adaptive-Hypergraphs

공식 코드

Decoupling Spatio-Temporal Prediction: When Lightweight Large Models Meet Adaptive Hypergraphs

https://github.com/SPEAR-IIT/CQSim

공식 코드

Deep Reinforcement Agent for Scheduling in HPC

https://github.com/SamYWK/Associated_Learning

공식 코드

Associated Learning: Decomposing End-to-end Backpropagation based on Auto-encoders and Target Propagation

https://github.com/SamuelFountain/FLStore

공식 코드

FLStore: Efficient Federated Learning Storage for non-training workloads

https://github.com/SeyedMuhammadHosseinMousavi/Victoria-Amazonica-Optimization-VAO-Algorithm

공식 코드

Victoria Amazonica Optimization (VAO): An Algorithm Inspired by the Giant Water Lily Plant

https://github.com/ShengrenHou/Energy-management-MIP-Deep-Reinforcement-Learning

공식 코드

Optimal Energy System Scheduling Using A Constraint-Aware Reinforcement Learning Algorithm

https://github.com/Soptq/e-3sfc

공식 코드

E-3SFC: Communication-Efficient Federated Learning with Double-way Features Synthesizing

https://github.com/Soumyabrata2003/TripCraft

공식 코드

TripCraft: A Benchmark for Spatio-Temporally Fine Grained Travel Planning

https://github.com/StephenEkaputra/Swapped-Logit-Distillation

공식 코드

Swapped Logit Distillation via Bi-level Teacher Alignment

https://github.com/SymbioticLab/Salus

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Salus: Fine-Grained GPU Sharing Primitives for Deep Learning Applications

https://github.com/TUDB-Labs/multi-lora-fine-tune

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mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs

https://github.com/TY-Cheng/torchvinecopulib

공식 코드

Vine Copulas as Differentiable Computational Graphs

https://github.com/Thesys-lab/Helix-ASPLOS25

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Helix: Serving Large Language Models over Heterogeneous GPUs and Network via Max-Flow

https://github.com/UniHD-CEG/gpu-mangrove

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A Simple Model for Portable and Fast Prediction of Execution Time and Power Consumption of GPU Kernels

https://github.com/VCA-EPFL/FSA

공식 코드

SystolicAttention: Fusing FlashAttention within a Single Systolic Array

https://github.com/Vencent-Won/KRGTS-public

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Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property Prediction

https://github.com/YMA33/CPU-GPU-SGD

공식 코드

Heterogeneous CPU+GPU Stochastic Gradient Descent Algorithms

https://github.com/YMA33/HeteroGPU

공식 코드

Adaptive Elastic Training for Sparse Deep Learning on Heterogeneous Multi-GPU Servers

https://github.com/Yaakoubi/Struct-CKN

공식 코드

Structured Convolutional Kernel Networks for Airline Crew Scheduling

https://github.com/Yu-Maryland/Differentiable_Scheduler_ICML24

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Differentiable Combinatorial Scheduling at Scale

https://github.com/ZanChaudhry/ExpTest

공식 코드

ExpTest: Automating Learning Rate Searching and Tuning with Insights from Linearized Neural Networks

https://github.com/aaltopml/human-in-the-loop-predictive-maintenance

공식 코드

Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations

https://github.com/aatizghimire/pluto-cluster

공식 코드

Cost-effective Deep Learning Infrastructure with NVIDIA GPU

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