Description
Reduce data training set requirements
Linked Case Studies
Case Study
Baidu
Baidu researchers synthesise speech through neural voice cloning with limited data samples
Case Study
Fast.AI
Fast.AI more accurately classifies text while requiring less training data due to a new natural language processing technique
Case Study
Facebook
Facebook reduces time needed to support new queries to its internal reactive cache by using machine learning from weeks to minutes
Case Study
University College London
UCL researchers optimise generated training set data for use in machine learning to decrease data requirements and improve model robustness
Case Study
MIT
MIT researchers propose an efficient and accurate system for protecting privacy in healthcare datasets
Case Study
Boston University
Researchers from Boston University improve automatic parameter selection for synthetic data creation