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/DeadAt0m/adafactor-pytorch
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
https://github.com/Deci-AI/super-gradients
https://github.com/DennisReddyhoff/ContinualGP
https://github.com/DongyoungLim/THEO_POULA
https://github.com/DreamInvoker/LookaheadOptimizer-mx-implementation
https://github.com/DudewithPigskin/EvolutionaryAlgorithms
Uncrowded Hypervolume-based Multi-objective Optimization with Gene-pool Optimal Mixing
https://github.com/DylanMuir/fmin_adam
https://github.com/ENSTA-U2IS-AI/torch-uncertainty
Averaging Weights Leads to Wider Optima and Better Generalization
https://github.com/Edresson/VoiceSplit
Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks
https://github.com/Evgeneus/MoCo
https://github.com/Fabrolly/DeepLearning-Experiment-on-Taiwan-Payaments-Information
https://github.com/Frager/Deep-Neuroevolution-In-SC2LE
https://github.com/GLambard/AdamW_Keras
https://github.com/GMC-DRL/ConfigX
ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning
https://github.com/GeorgOhneH/WerbeSkip
Averaging Weights Leads to Wider Optima and Better Generalization
https://github.com/Gjiangtao/A-Deep-Supervised-Edge-Optimization-Algorithm-for-Salt-Body-Segmentation
https://github.com/HRNet/Lite-HRNet
https://github.com/HamadYA/GhostFaceNets
https://github.com/Harshvardhan1/cyclic-learning-schedulers-pytorch
https://github.com/HelloDuoLA/SGDR-Stochastic-Gradient-Descent-with-Warm-Restarts
https://github.com/HilalAsi/APROX-Robust-Stochastic-Optimization-Algorithms
https://github.com/IBM/ZOSVRG-BlackBox-Adv
Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization
https://github.com/IOHprofiler/Analyzer
IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics
https://github.com/IOHprofiler/Post-Processing
IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics
https://github.com/IVRL/FG-NIC
https://github.com/JYWa/MATCHA
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication
https://github.com/Jarr0d/ViTAA
https://github.com/JiaYunSong/Gan-for-Gem-Generation
https://github.com/Jmak12/Iris1
https://github.com/JoeriHermans/dist-keras
https://github.com/JunnYu/paddle_convbert
https://github.com/K2OTO/Eve
Eve: A Gradient Based Optimization Method with Locally and Globally Adaptive Learning Rates
https://github.com/KarhouTam/FL-bench
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
https://github.com/KarhouTam/SCAFFOLD-PyTorch
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
https://github.com/KhalilJDamouni/RMSGD_Pretest
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.