activity
20202023
most citedDomino: Discovering Systematic Errors with Cross-Modal Embeddings

39 citations · 65 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2023

ViLLA: Fine-Grained Vision-Language Representation Learning from Real-World Data

Maya Varma, Jean-Benoit Delbrouck, Sarah Hooper +2

Vision-language models (VLMs), such as CLIP and ALIGN, are generally trained on datasets consisting of image-caption pairs obtained from the web. However, real-world multimodal dat…

cs.CL2022★ 11 cited

Toward expanding the scope of radiology report summarization to multiple anatomies and modalities

Zhihong Chen, Maya Varma, Xiang Wan +2

Radiology report summarization (RRS) is a growing area of research. Given the Findings section of a radiology report, the goal is to generate a summary (called an Impression sectio…

cs.LG2022★ 39 cited

Domino: Discovering Systematic Errors with Cross-Modal Embeddings

Sabri Eyuboglu, Maya Varma, Khaled Saab +5

Machine learning models that achieve high overall accuracy often make systematic errors on important subsets (or slices) of data. Identifying underperforming slices is particularly…

cs.CL2021

Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text

Maya Varma, Laurel Orr, Sen Wu +3

Named entity disambiguation (NED), which involves mapping textual mentions to structured entities, is particularly challenging in the medical domain due to the presence of rare ent…

cs.CV2020★ 14 cited

Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study

Peter Washington, Haik Kalantarian, John Kent +16

Background: Automated emotion classification could aid those who struggle to recognize emotions, including children with developmental behavioral conditions such as autism. However…

cs.CL2020★ 1 cited

Determining Question-Answer Plausibility in Crowdsourced Datasets Using Multi-Task Learning

Rachel Gardner, Maya Varma, Clare Zhu +1

Datasets extracted from social networks and online forums are often prone to the pitfalls of natural language, namely the presence of unstructured and noisy data. In this work, we…