As enterprises move from pilots to production, an AI Data Platform approach keeps data ready, governed, protected, and close ...

Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private data. The standard architecture — chunking documents, embedding them into a ...

Abstract: Support vector machines (SVMs) are successful supervised learning models that analyze data for classification and regression. Previous work has demonstrated the superiority of the SVMs in ...

This paper proposes a symmetric alternating direction method of multipliers with two different relaxation factors for solving nonconvex optimization problems with linear constraints and a ...

Abstract: A geometrical approach to teaching and learning vector calculus and analysis as applied to electromagnetic fields is proposed for junior-level undergraduate electromagnetics education. For ...

A new open-source framework called PageIndex solves one of the old problems of retrieval-augmented generation (RAG): handling very long documents. The classic RAG workflow (chunk documents, calculate ...

Large language models (LLMs) have significantly advanced natural language understanding and demonstrated strong problem-solving abilities. Despite these successes, most LLMs still struggle with ...

The rise of AI, graphic processing, combinatorial optimization and other data-intensive applications has resulted in data-processing bottlenecks, as ever greater amounts of data must be shuttled back ...

This paper proposes a family of line-search methods to deal with weighted orthogonal procrustes problems. In particular, the proposed family uses a search direction based on a convex combination ...

PRIMA is a package for solving general nonlinear optimization problems without using derivatives. It provides the reference implementation for Powell's renowned derivative-free optimization methods, i ...