This chapter surveys and analyses visual methods of explainability of Machine Learning
(ML) approaches with focus on moving from quasi-explanations that dominate in ML to …

We explore trust in a relatively new area of data science: Automated Machine Learning
(AutoML). In AutoML, AI methods are used to generate and optimize machine learning …

Recent advances in pre-trained language models have significantly improved neural
response generation. However, existing methods usually view the dialogue context as a …

Urgent transition from the dependence on fossil fuels towards renewable energies requires
more solar photovoltaic power to be connected to the electricity grids, with reliable supply …

Artificial Intelligence (AI) can now automate the algorithm selection, feature engineering, and
hyperparameter tuning steps in a machine learning workflow. Commonly known as AutoML …

As automated machine learning (AutoML) systems continue to progress in both
sophistication and performance, it becomes important to understand thehow'andwhy'of …

In recent years, a wide variety of automated machine learning (AutoML) methods have been
proposed to generate end-to-end ML pipelines. While these techniques facilitate the …

To mitigate the pain of manually tuning hyperparameters of deep neural networks,
automated machine learning (AutoML) methods have been developed to search for an …

As automated machine learning (AutoML) systems continue to progress in both
sophistication and performance, it becomes important to understand the 'how'and 'why'of …

During the training phase of machine learning (ML) models, it is usually necessary to
configure several hyperparameters. This process is computationally intensive and requires …