IEEE

Hyperparameter Tuning of LSTM Load Forecasting Model Based on Segmented Bayesian Optimization

Abstract: As a key link in power system operation and planning, the prediction accuracy of power load forecasting is directly related to the economy, security and power supply reliability of power ...
Scientific Research Publishing

Khaled, A. and Jin, C. (2024) Tuning-Free Stochastic Optimization.

ABSTRACT: Mathematical optimization is a fundamental aspect of machine learning (ML). An ML task can be conceptualized as optimizing a specific objective using the training dataset to discern patterns ...
GitHub

An AutoML framework for classical machine learning algorithms, automating model selection and hyperparameter tuning through Bayesian optimization, portfolio-based meta-learning ...

Start a Ray head node Connect and start Ray worker nodes via SSH Activate virtual environments and configure PYTHONPATH on all nodes đź“Ś Before running the script, ensure passwordless SSH access from ...
GitHub

hyperparameter-tuning

Spearmint integrated Bayesian Optimization for hyper parameter tuning of Auto sparse encoder embedded with softmax Classifier for MNIST digit Classification. Platform + GUI for hyperparameter ...
insideHPC

Using Bayesian Optimization to Tune Machine Learning Models

The presentation below, “Using Bayesian Optimization to Tune Machine Learning Models” by Scott Clark of SigOpt is from MLconf. The talk briefly introduces Bayesian Global Optimization as an efficient ...