https://github.com/HawkTom/VESAEA
공식 코드Voronoi-based Efficient Surrogate-assisted Evolutionary Algorithm for Very Expensive Problems
Papers with Code 아카이브
Stochastic Optimization
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Stochastic Optimization is the task of optimizing certain objective functional by generating and using stochastic random variables. Usually the Stochastic Optimization is an iterative process of generating random variables that progressively finds out the minima or the maxima of the objective functional. Stochastic Optimization is usually applied in the non-convex functional spaces where the usual deterministic optimization such as linear or quadratic programming or their variants cannot be used.
Source: ASOC: An Adaptive Parameter-free Stochastic Optimization Techinique for Continuous Variables
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/HikariX/MP-FedXGB
공식 코드https://github.com/IOHprofiler/IOHAnalyzer
공식 코드IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics
https://github.com/IOHprofiler/IOHalgorithm
공식 코드Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler
https://github.com/JoeMWatson/input-inference-for-control
공식 코드Efficient Stochastic Optimal Control through Approximate Bayesian Input Inference
https://github.com/JoseCPereira/2015EvolutionaryAlgorithmsJava
공식 코드https://github.com/JoseCPereira/2015ParameterlessEvolutionaryAlgorithmsJava
공식 코드A Java Implementation of Parameter-less Evolutionary Algorithms
https://github.com/JoseCPereira/2015ParameterlessEvolutionaryPortfolioJava
공식 코드Java Implementation of a Parameter-less Evolutionary Portfolio
https://github.com/Karl-Krauth/Sparse-GP
공식 코드https://github.com/Kipok/understanding-momentum
공식 코드Understanding the Role of Momentum in Stochastic Gradient Methods
https://github.com/KuluAndrej/NIPS-2019-code
공식 코드A Generic Acceleration Framework for Stochastic Composite Optimization
https://github.com/LIBBLE/LIBBLE-Spark
공식 코드SCOPE: Scalable Composite Optimization for Learning on Spark
https://github.com/LIVIAETS/extended_logbarrier
공식 코드Constrained Deep Networks: Lagrangian Optimization via Log-Barrier Extensions
https://github.com/LaurantChao/VIP
공식 코드https://github.com/Lingkai-Kong/SO-EBM
공식 코드https://github.com/LiyuanLucasLiu/RAdam
공식 코드https://github.com/LorenzoFederici/RobustTrajectoryDesignbyRL
공식 코드Reinforcement Learning for Low-Thrust Trajectory Design of Interplanetary Missions
https://github.com/Ludvins/FixedMeanGaussianProcesses
공식 코드Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep Learning
https://github.com/LuxLiang/SGHMC_discontinuous
공식 코드https://github.com/Lvcrezia77/PRODEN
공식 코드Progressive Identification of True Labels for Partial-Label Learning
https://github.com/MLOPTPSU/FedTorch
공식 코드Federated Learning with Compression: Unified Analysis and Sharp Guarantees
https://github.com/MatiasAlvo/Neural_inventory_control
공식 코드Neural Inventory Control in Networks via Hindsight Differentiable Policy Optimization
https://github.com/MilesCranmer/gnn_resource_allocation
공식 코드https://github.com/MingruiLiu-ML-Lab/Single-Loop-bilevel-Optimizer-under-Unbounded-Smoothness
공식 코드https://github.com/MohammadBeh/TO-TL-GUI
공식 코드Real-Time Topology Optimization in 3D via Deep Transfer Learning
https://github.com/MohammadSadeghSalehi/ISGD
공식 코드https://github.com/Muskman/costa-trajectory-planning
공식 코드Practical Precoding via Asynchronous Stochastic Successive Convex Approximation
https://github.com/NaitongChen/automated-coreset-mcmc-experiments
공식 코드https://github.com/NanxuGong/ELLM-FT
공식 코드Evolutionary Large Language Model for Automated Feature Transformation
https://github.com/NikolaKon1994/Robust-Learning-from-Untrusted-Sources
공식 코드https://github.com/OptimalPursuit/navigating_uncertainty_in_mpp
공식 코드https://github.com/Optimization-AI/LibAUC
공식 코드https://github.com/PaulEcoffet/RLCoopExp
공식 코드Policy Search with Rare Significant Events: Choosing the Right Partner to Cooperate with
https://github.com/PrincetonLIPS/RandomizedAutomaticDifferentiation
공식 코드https://github.com/ProbabilisticNumerics/cabs
공식 코드https://github.com/PurdueMINDS/JanossyPooling
공식 코드Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.