SAP aims to displace more general large language models with the release of its own foundational “tabular” model, which the company claims will reduce training requirements for enterprises. The model, ...

Abstract: In recent years, numerous model extraction attacks have been proposed to investigate the potential vulnerabilities of tabular models. However, applying these attacks in real-world scenarios ...

Model Context Protocol, or MCP, is arguably the most powerful innovation in AI integration to date, but sadly, its purpose and potential are largely misunderstood. So what's the best way to really ...

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 ...

Say you run a hospital and you want to estimate which patients have the highest risk of deterioration so that your staff can prioritize their care 1. You create a spreadsheet in which there is a row ...

Machine learning for predictive modeling aims to forecast outcomes based on input data accurately. One of the primary challenges in this field is “domain adaptation,” which addresses differences ...

In solving real-world data science problems, model selection is crucial. Tree ensemble models like XGBoost are traditionally favored for classification and regression for tabular data. Despite their ...

Abstract: Generative Adversarial Network (GAN) models have shown to be effective in a wide range of machine learning applications, and tabular data generation process has not been an exception.

In this supplemental lesson, you use the DAX Editor to define a custom Detail Rows Expression. A Detail Rows Expression is a property on a measure, providing end-users more information about the ...