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 …

Mechanistic models have been used for centuries to describe complex interconnected
processes, including biological ones. As the scope of these models has widened, so have …

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

Hyperparameter optimization constitutes a large part of typical modern machine learning
workflows. This arises from the fact that machine learning methods and corresponding …

As data science becomes more mainstream, there will be an ever-growing demand for data
science tools that are more accessible, flexible, and scalable. In response to this demand …

This paper presents a benchmark of supervised Automated Machine Learning (AutoML)
tools. Firstly, we analyze the characteristics of eight recent open-source AutoML tools (Auto …

Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules
such as renewable biofuels or anticancer drugs. However, traditional synthetic biology …

Sherpa is a hyperparameter optimization library for machine learning models. It is
specifically designed for problems with computationally expensive, iterative function …

New synthetic biology capabilities hold the promise of dramatically improving our ability to
engineer biological systems. However, a fundamental hurdle in realizing this potential is our …

Electroencephalography (EEG) is one of the most promising methods in the field of Brain-
Computer Interfaces (BCIs) due to its rich time-domain resolution and the availability of …