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/ylytkin/backpropagation
https://github.com/ymcui/LAMB_Optimizer_TF
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/yorkerlin/remove-the-square-root
https://github.com/ys-zong/medfair
https://github.com/yuhanghe01/OpenClustering
https://github.com/yujiaw98/fedcams
https://github.com/yumatsuoka/check_cosine_annealing_lr
https://github.com/yurakuratov/t5-experiments
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
https://github.com/yzhuoning/DeepAUC_OGB_Challenge
https://github.com/zaradana/Fast_BERT
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/zf020114/dardet
https://github.com/zhangtj1996/lookahead-sgd-adam-rmsprop-
https://github.com/zhuangdizhu/FedGen
https://github.com/zhuchen03/mva
https://github.com/ziyizhang27/dite-hrnet
https://github.com/zkxufo/TTM
https://github.com/zlwangustc/SWA_paddle
Averaging Weights Leads to Wider Optima and Better Generalization
https://github.com/zshuai8/FedGMM_ICML2023
https://github.com/zwx-open/Symmetric-Power-Transformation-INR
EVOS: Efficient Implicit Neural Training via EVOlutionary Selector
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.