SOURCE-LINKED INTELLIGENCE

Statistical Analysis of Generative Models

CORDIS · observation · Publication date unknown

Analysis of Generative Models Generative modeling, the automatic generation of examples such as texts, images, music, and molecules that are similar to those in a given dataset, is a central task in artificial intelligence. Mathematically, this task is framed as the problem of sampling from an unknown distribution, which is accessible only through a limited set of examples drawn from it. The size and quality of this set can vary greatly depending on the application. The algorithms that have propelled generative modeling to fame are known for their substantial data and computational resource requirements, often necessitating vast amounts of both to achieve state-of-the-art performance. The goal of this project is to investigate the mathematical properties of generative modeling algorithms to better understand their strengths and weaknesses, enhance their efficiency, and design new methods. The mathematical challenge in generative modeling lies in successfully integrating techniques from various areas of mathematical statistics and probability theory: dimension reduction, nonparamet

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recordType

award

status

SIGNED

region

EU

value

1535000

unit

EUR