Big tech companies — and startups — are increasingly using synthetic data to train their AI models. But there's risks to this strategy.

Regression is a method to estimate parameters in mathematical models of biological systems from experimental data. To ensure the validity of a model for a given data set, pre-regression and post ...

Using integrated modeling solutions to implement model-driven data management and development can help organizations share knowledge effectively and with minimum effort. It will certainly produce ...

This data model (i.e. "schema design,") is useful for developing applications around any restricted resource system, not just e-commerce systems.

It is imperative for health systems to form data sharing partnerships that allow for the analysis of de-identified clinical data, while effectively and comprehensively protecting patient information.

This paper considers the maximum likelihood estimation of panel data models with interactive effects. Motivated by applications in economics and other social sciences, a notable feature of the model ...

Discover what black box models are, their applications in finance and investing, and examples of how they drive ...

One of the most important steps in desiging a database is establishing the data model. Part one of a two-part article describes how to create a logical model.

Data poisoning is a type of attack that involves tampering with and polluting a machine learning model's training data, impacting the model's ability to produce accurate predictions.