Arbor separates strategy from execution using isolated git worktrees, so engineering teams can finally trace which ...

New framework helps biomanufacturers’ interventions, eyeing quality, cost, and stability for two-step chromatography decisions.

Abstract: Active policy search combines the trial-and-error methodology from policy search with Bayesian optimization to actively find the optimal policy. First ...

This repository provides simple examples of how to construct a configuration space using the ConfigSpace package, how to use BOHB with minimal efforts and how to run CAVE to generate a comprehensive ...

What would Thomas Bayes think? In 1763, he proposed a new approach to calculate probabilities. An international team has now updated his ideas to deliver a quantum Bayes' rule. (Courtesy: Centre for ...

Bayesian regression with linear basis function models. Introduction to Bayesian linear regression. Implementation with plain NumPy and scikit-learn. See also PyMC3 implementation. Gaussian processes.

Abstract: Controller tuning and parameter optimization are crucial in system design to improve closed-loop system performance. Bayesian optimization has been established as an efficient model-free ...

ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...

Researchers have used machine learning to design nano-architected materials that have the strength of carbon steel but the lightness of Styrofoam. The team describes how they made nanomaterials with ...

In the realm of machine learning, tuning a model to achieve optimal performance often involves navigating through a complex space of hyperparameters. One effective strategy for this is Bayesian ...

Feature selection is an indispensable step for the analysis of high-dimensional molecular data. Despite its importance, consensus is lacking on how to choose the most appropriate feature selection ...