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 ...
Cochrane

Bayesian Meta-Analysis: making it accessible for everyone!

This webinar introduced healthcare researchers to Bayesian meta-analysis methods, challenging the perception that these methods are inaccessible to non-statistical researchers. The session ...
Frontiers

Drug shortage in South Korea: machine learning-based prediction models and analysis of duration and causal factors

1 College of Pharmacy, Chung-Ang University, Seoul, Republic of Korea 2 College of Pharmacy, Sookmyung Women’s University, Seoul, Republic of Korea Background: Drug shortages remain a critical ...
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 ...
GitHub

[FR] Native Bayesian Hyperparameter Optimization Integration

Add native support for Bayesian hyperparameter optimization directly within MLflow, eliminating the need for external libraries like Optuna or Hyperopt. This feature would provide a deeply integrated ...
IEEE

Efficient Deep Learning Hyperparameter Tuning Using Cloud Infrastructure: Intelligent Distributed Hyperparameter Tuning with Bayesian Optimization in the Cloud

Abstract: The paper discusses how we can leverage cloud infrastructure for efficient hyperparameter tuning of deep neural networks on high dimensional hyperparameter spaces using Bayesian Optimization ...