When Machine Learning technologies are used in contexts that affect citizens, companies as
well as researchers need to be confident that there will not be any unexpected social …

Most machine learning algorithms are configured by a set of hyperparameters whose values
must be carefully chosen and which often considerably impact performance. To avoid a time …

Semi-supervised learning is the branch of machine learning concerned with using labelled
as well as unlabelled data to perform certain learning tasks. Conceptually situated between …

“Any AI smart enough to pass a Turing test is smart enough to know to fail it.”–*** Ian
McDonald Neural networks were developed to simulate the human nervous system for …

Deep learning (DL) techniques have obtained remarkable achievements on various tasks,
such as image recognition, object detection, and language modeling. However, building a …

This open access book presents the first comprehensive overview of general methods in
Automated Machine Learning (AutoML), collects descriptions of existing systems based on …

Recent interest in complex and computationally expensive machine learning models with
many hyperparameters, such as automated machine learning (AutoML) frameworks and …

Big Data applications are typically associated with systems involving large numbers of
users, massive complex software systems, and large-scale heterogeneous computing and …

H2O is an open source, distributed machine learning platform designed to scale to very
large datasets, with APIs in R, Python, Java and Scala. We present H2O AutoML, a highly …

Performance of machine learning algorithms depends critically on identifying a good set of
hyperparameters. While recent approaches use Bayesian optimization to adaptively select …