Abstract: Hyperparameter tuning is a crucial step in the development of machine learning models, as it directly impacts their performance and generalization ability. Traditional methods for ...

According to @godofprompt, a widespread trend in artificial intelligence research involves systematic p-hacking, where experiments are repeatedly run until benchmarks show improvement, with successes ...

Accurate channel-estimation algorithms are critical for enhancing the throughput of wireless communication systems, including millimetre wave (mmWave) multiple-input multiple-output (MIMO) systems, ...

Hyperparameter tuning is critical to the success of cross-device federated learning applications. Unfortunately, federated networks face issues of scale, heterogeneity, and privacy; addressing these ...

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 ...

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

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.