In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …

Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Signal processing, communications, and control have traditionally relied on classical
statistical modeling techniques. Such model-based methods utilize mathematical …

Sparse modeling for signal processing and machine learning, in general, has been at the
focus of scientific research for over two decades. Among others, supervised sparsity-aware …

Propelled partly by the Materials Genome Initiative, and partly by the algorithmic
developments and the resounding successes of data-driven efforts in other domains …

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data
from experiments, field measurements, and large-scale simulations at multiple …

Abstract Machine learning belongs to the crossroad of cybernetics (control science) and
computer science. It is attracting recently an overwhelming interest, both of professionals …

The next generation of telecommunication networks will integrate the latest developments
and emerging advancements in telecommunications connectivity infrastructures. In this …

Decoding brain activity from non-invasive electroencephalography (EEG) is crucial for brain-
computer interfaces (BCIs) and the study of brain disorders. Notably, end-to-end EEG …

Nowadays, the number of layers and of neurons in each layer of a deep network are
typically set manually. While very deep and wide networks have proven effective in general …