Kilpatrick’s John Erwin, Sara Beth Barnes, and Mikail Clark recently presented on the topic of “Corporate Transactions: Deal Structuring, Governance Documents ...

A new kind of large language model, developed by researchers at the Allen Institute for AI (Ai2), makes it possible to control how training data is used even after a model has been built.

When AI models fail to meet expectations, the first instinct may be to blame the algorithm. But the real culprit is often the data—specifically, how it’s labeled. Better data annotation—more accurate, ...

Abstract: Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems (ITS) in the real world. As a state-of-the-art generative ...

A team has developed a new method that facilitates and improves predictions of tabular data, especially for small data sets with fewer than 10,000 data points. The new AI model TabPFN is trained on ...

The Covid-19 pandemic reminded us that everyday life is full of interdependencies. The data models and logic for tracking the progress of the pandemic, understanding its spread in the population, ...

Abstract: Utilizing fake data (simulated based on mechanism models or generated through data-driven models) for data enhancement is a popular approach to solve the problem of fault diagnosis with ...