This report describes the physics case, the resulting detector requirements, and the evolving
detector concepts for the experimental program at the Electron-Ion Collider (EIC). The EIC …

Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

Reaction optimization is fundamental to synthetic chemistry, from optimizing the yield of
industrial processes to selecting conditions for the preparation of medicinal candidates …

One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …

Bayesian optimization is an approach to optimizing objective functions that take a long time
(minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of …

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

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 …

We report a method to convert discrete representations of molecules to and from a
multidimensional continuous representation. This model allows us to generate new …

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

The random forest (RF) algorithm has several hyperparameters that have to be set by the
user, for example, the number of observations drawn randomly for each tree and whether …