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.
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Bandit Structured Prediction for Neural Sequence-to-Sequence Learning
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Stochastic Optimization of Plain Convolutional Neural Networks with Simple methods
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Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
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Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization
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Averaging Weights Leads to Wider Optima and Better Generalization
https://github.com/kun-woo-park/Deeplearning_project_STL_10
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Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training
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