Nondeterministic Stack

"Stacking," in machine learning, refers to ensemble methods that sequentially build models, using the outputs of earlier models to inform the training of subsequent ones. Current research focuses on leveraging stacking for improved training efficiency in deep neural networks, enhancing the reasoning capabilities of language models, and boosting the performance of various prediction tasks, including mutagenicity prediction and load forecasting. These advancements are significant because stacking offers a powerful technique for improving model accuracy and efficiency across diverse applications, from drug discovery to resource management.