As our dependence on intelligent machines continues to grow, so does the demand for more
transparent and interpretable models. In addition, the ability to explain the model generally …

Despite great progress in simulating multiphysics problems using the numerical
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …

Researchers are defining new types of interactions between humans and machine learning
algorithms generically called human-in-the-loop machine learning. Depending on who is in …

International challenges have become the de facto standard for comparative assessment of
image analysis algorithms. Although segmentation is the most widely investigated medical …

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

Efficient anomaly detection and diagnosis in multivariate time-series data is of great
importance for modern industrial applications. However, building a system that is able to …

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 Natural Language Processing (NLP) is one of the most captivating applications of
Deep Learning. In this survey, we consider how the Data Augmentation training strategy can …

Emotion recognition technology through analyzing the EEG signal is currently an essential
concept in Artificial Intelligence and holds great potential in emotional health care, human …

Due to limited resources and environmental pollution, monitoring the geological
environment has become essential for many countries' sustainable development. As various …