Transformer on MSNOpinion

Against the METR graph

METR’s benchmark has become a bellwether of AI capability growth, but its design isn’t up to the task, argues Nathan Witkin ...
Scientific Research Publishing

Rahimloo, P. and Jafarian, A. (2016) Prediction of Diabetes by Using Artificial Neural Network, Logistic Regression Statistical Model and Combination of Them.

ABSTRACT: This paper aims to investigate the effectiveness of logistic regression and discriminant analysis in predicting diabetes in patients using a diabetes dataset. Additionally, the paper ...
SiliconANGLE

Nvidia debuts next-gen agentic AI and reasoning robotic models at SIGGRAPH 2025

Nvidia Corp. announced today that it’s expanding its offerings of smarter AI models, physical intelligence for robotics and powerful enterprise AI servers. Leading the news at the ACM SIGGRAPH 2025 ...
IEEE

Simplified Graph Contrastive Learning Model Without Augmentation

Abstract: Burgeoning graph contrastive learning (GCL) stands out in the graph domain with low annotated costs and high model performance improvements, which is typically composed of three standard ...
Searchenginejournal.com

Google’s New Graph Foundation Model Improves Precision By Up To 40X

Google published details of a new kind of AI based on graphs called a Graph Foundation Model (GFM) that generalizes to previously unseen graphs and delivers a three to forty times boost in precision ...
IEEE

Consistent Graph Model Generation with Large Language Models

Abstract: Graph model generation from natural language requirements is an essential task in software engineering, for which large language models (LLMs) have become increasingly popular. A key ...
marktechpost

KGGen: Advancing Knowledge Graph Extraction with Language Models and Clustering Techniques

Knowledge graphs (KGs) are the foundation of artificial intelligence applications but are incomplete and sparse, affecting their effectiveness. Well-established KGs such as DBpedia and Wikidata lack ...