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Using Known Information to Accelerate HyperParameters Optimization Based on SMBO
In this paper, we use gradient information and machine learning model analysis information to accelerate traditional hyperparameter optimization methods SMBO.
Asynch-SGBDT: Asynchronous Parallel Stochastic Gradient Boosting Decision Tree based on Parameters Server
One of the most important machine learning algorithms is Gradient Boosting Decision Tree, i. e. GBDT whose training process needs considerable computational resources and time.
Weighted parallel SGD for distributed unbalanced-workload training system
Stochastic gradient descent (SGD) is a popular stochastic optimization method in machine learning.
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