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 …

Abstract Gradient Boosted Decision Trees (GBDT's) are a powerful tool for classification and
regression tasks in Big Data. Researchers should be familiar with the strengths and …

While deep learning has enabled tremendous progress on text and image datasets, its
superiority on tabular data is not clear. We contribute extensive benchmarks of standard and …

Heterogeneous tabular data are the most commonly used form of data and are essential for
numerous critical and computationally demanding applications. On homogeneous datasets …

Tabular data is one of the most commonly used types of data in machine learning. Despite
recent advances in neural nets (NNs) for tabular data, there is still an active discussion on …

This survey explores how Deep Learning has battled the COVID-19 pandemic and provides
directions for future research on COVID-19. We cover Deep Learning applications in Natural …

Biochar materials have recently received considerable recognition as eco-friendly and cost-
effective adsorbents capable of effectively removing hazardous emerging contaminants (eg …

This article outlines our point of view regarding the applicability, state-of-the-art, and
potential of quantum computing for problems in finance. We provide an introduction to …

Many machine learning algorithms and almost all deep learning architectures are incapable
of processing plain texts in their raw form. This means that their input to the algorithms must …

The history of machine learning (ML) can be traced back to the 1950 s, and its application in
alloy design has recently begun to flourish and expand rapidly. The driving force behind this …