Biological organisms learn from interactions with their environment throughout their lifetime.
For artificial systems to successfully act and adapt in the real world, it is desirable to similarly …

Globally, there is a huge unmet need for effective treatments for neurodegenerative
diseases. The complexity of the molecular mechanisms underlying neuronal degeneration …

One of the main challenges facing the current approaches of speech emotion recognition is
the lack of a dataset large enough to train the currently available deep learning models …

We aimed at learning deep emotion features to recognize speech emotion. Two
convolutional neural network and long short-term memory (CNN LSTM) networks, one 1D …

Abstract Machine learning has become a pivotal tool for many projects in computational
biology, bioinformatics, and health informatics. Nevertheless, beginners and biomedical …

The fundamental mathematical tools needed to understand machine learning include linear
algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …

Deep learning is rapidly becoming a go-to tool for many artificial intelligence problems due
to its ability to outperform other approaches and even humans at many problems. Despite its …

We propose a quantum algorithm to solve systems of nonlinear differential equations. Using
a quantum feature map encoding, we define functions as expectation values of parametrized …

In structural engineering, model updating is often used for non-destructive damage
assessment: by calibrating stiffness parameters of finite element models based on …

Inverse problems are mathematical problems that arise when our goal is to recover “interior”
or “hidden” information from “outside”—or otherwise available—noisy data. For example, an …