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/XuezheMax/fairseq-apollo
https://github.com/Xunpeng746/ACMo
ACMo: Angle-Calibrated Moment Methods for Stochastic Optimization
https://github.com/YeLyuUT/VOSDetectron
https://github.com/ZMpursue/Anime_avatar_generation
https://github.com/ZainRaza14/Neuro-Evolution
https://github.com/ZoneTsuyoshi/pyassim
Kernel embedding of maps for sequential Bayesian inference: The variational mapping particle filter
https://github.com/aadharna/UntouchableThunder
https://github.com/aai-institute/pyDVL
Optimizing Neural Networks with Kronecker-factored Approximate Curvature
https://github.com/abcp4/MyDarts
https://github.com/abhuse/cyclic-cosine-decay
https://github.com/adaptive-intelligent-robotics/dcg-map-elites
Diversity Policy Gradient for Sample Efficient Quality-Diversity Optimization
https://github.com/adelnabli/acid
https://github.com/adelnabli/dadao
Optimal algorithms for smooth and strongly convex distributed optimization in networks
https://github.com/ahmad-aghapour/DeepPDE/tree/main/Deep%20Stochastic%20Optimization%20in%20Finance
https://github.com/ai4co/reevo
ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution
https://github.com/albangossard/bindings-nufft-pytorch
Efficient approximation of Jacobian matrices involving a non-uniform fast Fourier transform (NUFFT)
https://github.com/alecwangcq/KFAC-Pytorch
Optimizing Neural Networks with Kronecker-factored Approximate Curvature
https://github.com/alexandonian/pretorched-x
https://github.com/alisterta/Persian-text-to-speech
https://github.com/alldbi/SuperMix
https://github.com/allen108108/Model-Optimizer_Implementation
https://github.com/alpaficia/MOSEAC_Limo
https://github.com/alphadl/lookahead.pytorch
https://github.com/alshedivat/DeterminantalPointProcesses.jl
https://github.com/alstonlo/torch-influence
Second-Order Stochastic Optimization for Machine Learning in Linear Time
https://github.com/amidos2006/Mario-AI-Framework
https://github.com/amplab/cyclades
https://github.com/anandsaha/nips.cocob.pytorch
Training Deep Networks without Learning Rates Through Coin Betting
https://github.com/anathan90/RADiSA
Optimization for Large-Scale Machine Learning with Distributed Features and Observations
https://github.com/andreblumenau/Contemporary-Neural-Network
https://github.com/anonDEHB/DEHB
Regularized Evolution for Image Classifier Architecture Search
https://github.com/anonNo2/MulTeacher-KD
https://github.com/anonymous2020new/iffDetector
https://github.com/aorvieto/SGD-SVRG-models
Continuous-time Models for Stochastic Optimization Algorithms
https://github.com/apoorvagnihotri/big-little-net
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.