Abstract: Data augmentation is an effective way to overcome the overfitting problem of deep learning models. However, most existing studies on data augmentation work on framelike data (e.g., images), ...

Abstract: A training set with sufficient quantity and reliable quality is crucial for achieving satisfactory results in data-driven acoustic impedance (AI) inversion. However, effective data ...

While advances in artificial intelligence have been slow to reach commercial P&C insurance, new trends in data augmentation could help pick up the pace, according to experts on a recent Insurtech ...

Anchoring provides a steady start, grounding decisions and perspectives in clarity and confidence. Anchoring provides a steady start, grounding decisions and perspectives in clarity and confidence.

The AI revolution that we’re currently living through is a direct result of the explosion in the amount of data that’s available to be mined and analyzed for insights. However, collecting data from ...

New research from the Data Provenance Initiative has found a dramatic drop in content made available to the collections used to build artificial intelligence. By Kevin Roose Reporting from San ...

As an executive at a leading talent intelligence platform, I've had the privilege of witnessing firsthand the profound transformation HR technology has undergone over the years. Initially, the focus ...

Georgia Tech researchers Vidya Muthukumar and Eva Dyer are leading a multi-institutional project to develop a theory for data augmentation, aiming to improve the generalization and fairness of AI ...

Introduction: In recent years, the use of EEG signals for seizure detection has gained widespread academic attention. Aiming at the problem of overfitting deep learning models due to the small number ...