5 papers
Towards Universal Tabular Embeddings: A Benchmark Across Data Tasks
Liane Vogel, Kavitha Srinivas, Niharika D'Souza +3
Tabular foundation models aim to learn universal representations of tabular data that transfer across tasks and domains, enabling applications such as table retrieval, semantic sea…
DPDisc: From Factoid Questions to Data Product Requests for Open-World Data Product Discovery over Tables and Text
Liangliang Zhang, Nandana Mihindukulasooriya, Niharika S. D'Souza +4
Data products are reusable, self-contained assets designed for specific business use cases. Automating their discovery is of great industry interest, as it enables efficient data a…
Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025
Emily Alsentzer, Marie-Laure Charpignon, Bill Chen +90
The 6th Annual Conference on Health, Inference, and Learning (CHIL 2025), hosted by the Association for Health Learning and Inference (AHLI), was held in person on June 25-27, 2025…
Phrase-grounded Fact-checking for Automatically Generated Chest X-ray Reports
Razi Mahmood, Diego Machado-Reyes, Joy Wu +7
With the emergence of large-scale vision language models (VLM), it is now possible to produce realistic-looking radiology reports for chest X-ray images. However, their clinical tr…
Anatomically-Grounded Fact Checking of Automated Chest X-ray Reports
R. Mahmood, K. C. L. Wong, D. M. Reyes +8
With the emergence of large-scale vision-language models, realistic radiology reports may be generated using only medical images as input guided by simple prompts. However, their p…