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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/ARM-software/mango

공식 코드

MANGO: A Python Library for Parallel Hyperparameter Tuning

https://github.com/ASISys/Adrenaline

공식 코드

Injecting Adrenaline into LLM Serving: Boosting Resource Utilization and Throughput via Attention Disaggregation

https://github.com/AlgTUDelft/B-FELSA

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Benchmarking Flexible Electric Loads Scheduling Algorithms under Market Price Uncertainty

https://github.com/AslantheAslan/beta-GNN

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$β$-GNN: A Robust Ensemble Approach Against Graph Structure Perturbation

https://github.com/Atharva-Gundawar/LLM-Modulo-prompts

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Robust Planning with Compound LLM Architectures: An LLM-Modulo Approach

https://github.com/BIGBALLON/HTD

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Stochastic Gradient Descent with Hyperbolic-Tangent Decay on Classification

https://github.com/BNN-UPC/RouteNet-Fermi

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RouteNet-Fermi: Network Modeling with Graph Neural Networks

https://github.com/BenoitChoffin/das3h

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DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skills

https://github.com/Buzz-Beater/LEMMA

공식 코드

LEMMA: A Multi-view Dataset for Learning Multi-agent Multi-task Activities

https://github.com/CBCodeHub/Mining-Gym

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Mining-Gym: A Configurable RL Benchmarking Environment for Truck Dispatch Scheduling

https://github.com/CTU-IIG/RCPSPCpu

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Solving the Resource Constrained Project Scheduling Problem Using the Parallel Tabu Search Designed for the CUDA Platform

https://github.com/ChineseBest/AT-504

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Apply Artificial Neural Network to Solving Manpower Scheduling Problem

https://github.com/ChineseBest/AT-505

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An Intelligent Model for Solving Manpower Scheduling Problems

https://github.com/Cloudslab/DLSF

공식 코드

Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments using A3C learning and Residual Recurrent Neural Networks

https://github.com/DIR-LAB/deep-batch-scheduler

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RLScheduler: An Automated HPC Batch Job Scheduler Using Reinforcement Learning

https://github.com/DS3Lab/DT-FM

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Decentralized Training of Foundation Models in Heterogeneous Environments

https://github.com/EECi/VOI-for-Building-Energy

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Value of Information Analysis for rationalising information gathering in building energy analysis

https://github.com/EVERGi/predict-then-schedule-university-activities

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Evolutionary scheduling of university activities based on consumption forecasts to minimise electricity costs

https://github.com/FedML-AI/FedML

공식 코드

FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training

https://github.com/GeminiLight/flag-vne

공식 코드

FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation

https://github.com/GilesStrong/HiggsML_Lumin

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On the impact of selected modern deep-learning techniques to the performance and celerity of classification models in an experimental high-energy physics use case

https://github.com/Giventicket/BlackoutDIFUSCO

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Blackout DIFUSCO

https://github.com/HKBU-HPML/FADNet

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FADNet: A Fast and Accurate Network for Disparity Estimation

https://github.com/HKBU-HPML/ddl-benchmarks

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A Quantitative Survey of Communication Optimizations in Distributed Deep Learning

https://github.com/IBM/IPC-graph-data

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Online Planner Selection with Graph Neural Networks and Adaptive Scheduling

https://github.com/IBM/composite-adv

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Towards Compositional Adversarial Robustness: Generalizing Adversarial Training to Composite Semantic Perturbations

https://github.com/JayMan91/aaai_predit_then_optimize

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Smart Predict-and-Optimize for Hard Combinatorial Optimization Problems

https://github.com/Jiaju-Chen/UpliftRec

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Treatment Effect Estimation for User Interest Exploration on Recommender Systems

https://github.com/JorikJooken/MCTS01Knapsack

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Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems

https://github.com/JorikJooken/MCTSQuayCraneSchedulingNonCrossingConstraints

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Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems

https://github.com/LGE-ARC-AdvancedAI/auptimizer

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Auptimizer -- an Extensible, Open-Source Framework for Hyperparameter Tuning

https://github.com/Lhongpei/ipps-drl

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Solving Integrated Process Planning and Scheduling Problem via Graph Neural Network Based Deep Reinforcement Learning

https://github.com/Lingkai-Kong/SO-EBM

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End-to-End Stochastic Optimization with Energy-Based Model

https://github.com/LooperXX/DF-Net

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Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog

https://github.com/MachineLearningBCAM/Load-forecasting-IEEE-TPWRS-2020

공식 코드

Probabilistic Load Forecasting Based on Adaptive Online Learning

https://github.com/ManhqhUMich12/ANN_Based_GIE_AdaptiveControlGramework_VSG

공식 코드

ANN-Based Grid Impedance Estimation for Adaptive Gain Scheduling in VSG Under Dynamic Grid Conditions

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