Artificial intelligence (AI) has significantly impacted thyroid cancer diagnosis in recent years,
offering advanced tools and methodologies that promise to revolutionize patient outcomes …

Neural architecture search (NAS) has attracted increasing attention. In recent years,
individual search methods have been replaced by weight-sharing search methods for higher …

Abstract Computational Pathology (CPath) is an interdisciplinary science that augments
developments of computational approaches to analyze and model medical histopathology …

In astronomy, neural networks are often trained on simulation data with the prospect of being
used on telescope observations. Unfortunately, training a model on simulation data and then …

In this work we address the problem of accelerating complex power-grid simulation through
machine learning (ML). Specifically, we develop a framework, Smart-PGSim, which …

The recent emergence of deep learning to characterize complex patterns of protein big data
reveals its potential to address the classic challenges in the field of protein data mining …

Developing surrogate geophysical models from data is a key research topic in atmospheric
and oceanic modeling because of the large computational costs associated with numerical …

Deep learning has emerged as a powerful method for extracting valuable information from
large volumes of data. However, when new training data arrives continuously (ie, is not fully …

Large-scale measurements linking genetic background to biological function have driven a
need for models that can incorporate these data for reliable predictions and insight into the …

Developing high-performing predictive models for large tabular data sets is a challenging
task. Neural architecture search (NAS) is an AutoML approach that generates and evaluates …