Machine learning models are increasingly applied across scientific disciplines, yet their effectiveness often hinges on heuristic decisions such as data transformations, training strategies, and model ...

ABSTRACT: The accurate prediction of backbreak, a crucial parameter in mining operations, has a significant influence on safety and operational efficiency. The occurrence of this phenomenon is ...

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 ...

self_optimizing_pipeline/ ├── data/ # Sample dataset (train/test) │ ├── sample_data.csv # Example dataset ├── src/ # Source code modules │ ├── preprocess.py # Data cleaning & preprocessing │ ├── ...

In machine learning, algorithms harness the power to unearth hidden insights and predictions from within data. Central to the effectiveness of these algorithms are hyperparameters, which can be ...

Abstract: Selection of hyperparameters in deep neural networks is a challenging problem due to the wide search space and emergence of various layers with specific hyperparameters. There exists an ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Markov state models (MSM) are a popular statistical method for analyzing the ...