Abstract Machine learning (ML) is a new-age thriving technology, which facilitates
computers to read and interpret from the previously present data automatically. It makes use …

Globally, there is a substantial unmet need to diagnose various diseases effectively. The
complexity of the different disease mechanisms and underlying symptoms of the patient …

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

Interpretability in machine learning (ML) is crucial for high stakes decisions and
troubleshooting. In this work, we provide fundamental principles for interpretable ML, and …

This textbook establishes a theoretical framework for understanding deep learning models
of practical relevance. With an approach that borrows from theoretical physics, Roberts and …

Abstract Neural Radiance Fields (NeRFs) have achieved great success in the past few
years. However, most current methods still require intensive resources due to ray marching …

Machine learning has become a crucial tool in drug discovery and chemistry at large, eg, to
predict molecular properties, such as bioactivity, with high accuracy. However, activity …

With the down-scaling of CMOS technology, the design complexity of very large-scale
integrated is increasing. Although the application of machine learning (ML) techniques in …

Machine learning has emerged with big data technologies and high-performance computing
to create new opportunities for data intensive science in the multi-disciplinary agri …

Building energy consumption modeling and forecasting is essential to address buildings
energy efficiency problems and take up current challenges of human comfort, urbanization …