However, traditional clinical trial designs are often ill-suited for rare disease research with common challenges including ...

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 ...

Currently, deep learning is the most important technique for solving many complex machine vision problems. State-of-the-art deep learning models typically contain a very large number of parameters ...

A conditional generative adversarial network architecture was implemented to generate synthetic data. Use cases were myelodysplastic syndromes (MDS) and AML: 7,133 patients were included. A fully ...

The task of point cloud classification suffers from the problem of insufficient data, and data augmentation is an effective method to alleviate this problem. However, the effect of conventional ...

Synthetic data generation (SDG) was proposed in the early nineties as a form of imputation. 1 Since then, multiple statistical and machine learning (ML) methods have been developed to generate ...

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 ...

A new technical paper titled “An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design” was published by researchers at the University of Texas at Austin, Nvidia ...

Ambuj Tewari receives funding from NSF and NIH. You’ve just finished a strenuous hike to the top of a mountain. You’re exhausted but elated. The view of the city below is gorgeous, and you want to ...