779 citations
- Massachusetts Institute of TechnologyUS13 papers
- Harvard UniversityUS10 papers
- Stanford UniversityUS10 papers
- Technical University of MunichDE9 papers
- Beijing Academy of Artificial IntelligenceCN8 papers
- Monash UniversityAU8 papers
- Shanghai Jiao Tong UniversityCN8 papers
- University of Hong KongHK8 papers
- Harvard University PressUS7 papers
- University of Southern CaliforniaUS7 papers
- Hong Kong University of Science and TechnologyHK6 papers
- Imperial College LondonGB6 papers
7 papers · 2 filters
The Open Syndrome Definition
Ana Paula Gomes Ferreira, Aleksandar Anžel, Izabel Oliva Marcilio de Souza +6
Case definitions are essential for effectively communicating public health threats. However, the absence of a standardized, machine-readable format poses significant challenges to…
Evaluating Compliance with Visualization Guidelines in Diagrams for Scientific Publications Using Large Vision Language Models
Johannes Rückert, Louise Bloch, Christoph M. Friedrich
Diagrams are widely used to visualize data in publications. The research field of data visualization deals with defining principles and guidelines for the creation and use of these…
AggTruth: Contextual Hallucination Detection using Aggregated Attention Scores in LLMs
Piotr Matys, Jan Eliasz, Konrad Kiełczyński +4
In real-world applications, Large Language Models (LLMs) often hallucinate, even in Retrieval-Augmented Generation (RAG) settings, which poses a significant challenge to their depl…
GATES: Cost-aware Dynamic Workflow Scheduling via Graph Attention Networks and Evolution Strategy
Ya Shen, Gang Chen, Hui Ma +1
Cost-aware Dynamic Workflow Scheduling (CADWS) is a key challenge in cloud computing, focusing on devising an effective scheduling policy to efficiently schedule dynamically arrivi…
Exploring a Large Language Model for Transforming Taxonomic Data into OWL: Lessons Learned and Implications for Ontology Development
Filipi Miranda Soares, Antonio Mauro Saraiva, Luís Ferreira Pires +6
Managing scientific names in ontologies that represent species taxonomies is challenging due to the ever-evolving nature of these taxonomies. Manually maintaining these names becom…
A Framework for the Assurance of AI-Enabled Systems
Ariel S. Kapusta, David Jin, Peter M. Teague +4
The United States Department of Defense (DOD) looks to accelerate the development and deployment of AI capabilities across a wide spectrum of defense applications to maintain strat…