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://bitbucket.org/mateuszzaborski/pslshade
공식 코드https://github.com/1589864500/sdes
공식 코드Scaling Multi-Objective Security Games Provably via Space Discretization Based Evolutionary Search
https://github.com/8uurg/Multistructured-Problems-LK-GOMEA
공식 코드Solving Multi-Structured Problems by Introducing Linkage Kernels into GOMEA
https://github.com/99991/cifar10-fast-simple
공식 코드A Novel Unified Parametric Assumption for Nonconvex Optimization
https://github.com/AGI-Arena/MARS
공식 코드MARS: Unleashing the Power of Variance Reduction for Training Large Models
https://github.com/ARM-software/mango
공식 코드https://github.com/Adamdad/Filter-Gradient-Decent
공식 코드Stochastic Gradient Variance Reduction by Solving a Filtering Problem
https://github.com/AlanLahoud/BNNSOP
공식 코드Learning Solutions of Stochastic Optimization Problems with Bayesian Neural Networks
https://github.com/Antymon/ppo_cpp
공식 코드https://github.com/ArkadiyD/MacroNASBenchmark
공식 코드Local Search is a Remarkably Strong Baseline for Neural Architecture Search
https://github.com/BaguaSys/bagua
공식 코드BAGUA: Scaling up Distributed Learning with System Relaxations
https://github.com/BaoWangMath/DP-LSSGD
공식 코드DP-LSSGD: A Stochastic Optimization Method to Lift the Utility in Privacy-Preserving ERM
https://github.com/BenBals/Towards-Explainable-Real-Estate
공식 코드Towards Explainable Real Estate Valuation via Evolutionary Algorithms
https://github.com/Bhattacharya-Lab/PolyFold
공식 코드PolyFold: an interactive visual simulator for distance-based protein folding
https://github.com/Bio4Res/pedestrian-evacuation-optimization
공식 코드Evolutionary Algorithms for Optimizing Emergency Exit Placement in Indoor Environments
https://github.com/CMU-SAFARI/PIM-Opt
공식 코드https://github.com/CarolaDoerr/2019-LO-SelfAdjusting
공식 코드Self-Adjusting Mutation Rates with Provably Optimal Success Rules
https://github.com/CausalML/StochOptForest
공식 코드https://github.com/CharlieDinh/FEDL
공식 코드Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation
https://github.com/CharlieDinh/pFedMe
공식 코드https://github.com/ClippedStochasticMethods/clipped-SSTM
공식 코드High Probability Complexity Bounds for Non-Smooth Stochastic Optimization with Heavy-Tailed Noise
https://github.com/Cosijopiii/CGPNAS
공식 코드https://github.com/CyberAgentAILab/cmaes
공식 코드CatCMA : Stochastic Optimization for Mixed-Category Problems
https://github.com/D-X-Y/ResNeXt-DenseNet
공식 코드https://github.com/DIOL-UniTN/llm_step_size_adaptation
공식 코드https://github.com/DTUComputeTONIA/StagnationDetection
공식 코드Self-Adjusting Evolutionary Algorithms for Multimodal Optimization
https://github.com/Daniboy370/Masters-Project
공식 코드A Study of a Genetic Algorithm for Polydisperse Spray Flames
https://github.com/DanieleGammelli/multi-output-gp-censored-regression
공식 코드Generalized Multi-Output Gaussian Process Censored Regression
https://github.com/DongyoungLim/TUSLA_RELU
공식 코드https://github.com/Evolving-AI-Lab/fooling
공식 코드Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
https://github.com/FeiLiu36/LLM4MOEA
공식 코드Large Language Model for Multi-objective Evolutionary Optimization
https://github.com/FurongYe/irace-1
공식 코드https://github.com/Goda-Research-Group/MLMC_stochastic_gradient
공식 코드Unbiased MLMC stochastic gradient-based optimization of Bayesian experimental designs
https://github.com/HanbaekLyu/SRMM
공식 코드Stochastic regularized majorization-minimization with weakly convex and multi-convex surrogates
https://github.com/HaokunChen245/FedDAT
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.