Nature

A manta ray-bayesian optimization approach for hyperparameter-tuned convolutional neural networks in lung cancer classification

Lung cancer remains a global health challenge that is unavoidable. Despite the advances in lung cancer classification using deep learning models, the performance remains highly dependent on ...
Nature

Best practices for multi-fidelity Bayesian optimization in materials and molecular research

Among machine learning techniques, Bayesian optimization (BO) has emerged as the go-to choice for optimizing the design of experiments in the chemical domain 1,2,3,4. BO, grounded in a probabilistic ...
LinkedIn

Optimizing Machine Learning Models with Bayesian Optimization: A Deep Dive into Gaussian Processes and Hyperparameter Tuning

Hyperparameter tuning is one of the most critical aspects of training a machine learning model. Properly tuned hyperparameters can make the difference between a mediocre model and one that achieves ...
IEEE

Graphical Causal Model Based Bayesian Optimization for Industrial Process Parameterization

Abstract: Optimizing production processes is crucial for reducing costs, ensuring product quality, and minimizing downtime. Optimization approaches based on manual parameterization are increasingly ...
IEEE

Evolutionary Federated Bayesian Optimization

Abstract: As awareness of data privacy protection continues to grow, many-task optimization faces a significant challenge in balancing privacy protection and performance improvement. This paper ...
LinkedIn

Bayesian Thinking-5: Optimization in Pharmaceutical Development: Reducing Experiments from 25 to 10 — Reshaping the Paradigm of Drug Formulation

In traditional pharmaceutical development, limitations of Design of Experiments (DOE) have become a critical bottleneck. Consider the development of orally disintegrating tablets (ODTs): conventional ...