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/KnightZhang625/DARTS_NAS_TextClassification
https://github.com/Koukyosyumei/AIJack
https://github.com/Liuhong99/Sophia
Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training
https://github.com/MLOPTPSU/TorchFed
https://github.com/MS-Mind/MS-Code-05
https://github.com/MalayAgr/DeepNeuralNetwork-Scratch
https://github.com/MarcAntoineAlex/darts
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Adam_a_Method_For_Stochastic_Optimization
https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/On_the_Variance_of_the_Adaptive_Learning_Rate_and_Beyond
https://github.com/MediaBrain-SJTU/FedLESAM
https://github.com/MichaelKonobeev/adashift
https://github.com/Mind23-2/MindCode-107
https://github.com/Mind23-2/MindCode-5/tree/main/pnasnet
https://github.com/MindCode-4/code-11/tree/main/adam-aio
https://github.com/MindCode-4/code-12/tree/main/Large-Batch-Optimization-for-Deep-Learning
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/MindCode-4/code-12/tree/main/gaussian_adaptive_attention
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/MindCode-4/code-13/tree/main/SGDR
https://github.com/MindCode-4/code-6/tree/main/Black-Box-Attacks-against
https://github.com/MindCode-4/code-6/tree/main/adam-aio
https://github.com/MindCode-4/code-7/tree/main/Large-Batch-Optimization-for-Deep-Learning
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/MindCode-4/code-9/tree/main/SGDR
https://github.com/MindSpore-scientific-2/code-12/tree/main/Large-Batch-Optimization-for-Deep-Learning
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/MindSpore-scientific-2/code-12/tree/main/gaussian_adaptive_attention
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/MindSpore-scientific-2/code-2/tree/main/SVGD
SVGD: A Virtual Gradients Descent Method for Stochastic Optimization
https://github.com/MindSpore-scientific/code-10/tree/main/SVGD
SVGD: A Virtual Gradients Descent Method for Stochastic Optimization
https://github.com/MindSpore-scientific/code-10/tree/main/pytorch_convgru
https://github.com/MindSpore-scientific/code-2/tree/main/stochastic-attention-head-removal-a-simple
Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training
https://github.com/MindSpore-scientific/code-3/tree/main/adam-aio
https://github.com/Morxrc/kaggle_generate-dogs_public-1st-private-16th-solution
Averaging Weights Leads to Wider Optima and Better Generalization
https://github.com/MrtnMndt/Rethinking_CNN_Layerwise_Feature_Amounts
https://github.com/NVIDIA/OpenSeq2Seq
Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks
https://github.com/NVIDIA/retinanet-examples
https://github.com/NajibYavari/DD2412
Averaging Weights Leads to Wider Optima and Better Generalization
https://github.com/OceanSummerDay/HSTA_MER
https://github.com/Optimization-AI/NeurIPS2021_SOAP
Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.