Search Results for author: Maya Varma

Found 6 papers, 5 papers with code

Domino: Discovering Systematic Errors with Cross-Modal Embeddings

2 code implementations ICLR 2022 Sabri Eyuboglu, Maya Varma, Khaled Saab, Jean-Benoit Delbrouck, Christopher Lee-Messer, Jared Dunnmon, James Zou, Christopher Ré

In this work, we address these challenges by first designing a principled evaluation framework that enables a quantitative comparison of SDMs across 1, 235 slice discovery settings in three input domains (natural images, medical images, and time-series data).

Representation Learning Time Series Analysis

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

1 code implementation Findings (EMNLP) 2021 Maya Varma, Laurel Orr, Sen Wu, Megan Leszczynski, Xiao Ling, Christopher Ré

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 entities.

Data Integration Entity Disambiguation

Training an Emotion Detection Classifier using Frames from a Mobile Therapeutic Game for Children with Developmental Disorders

no code implementations16 Dec 2020 Peter Washington, Haik Kalantarian, Jack Kent, Arman Husic, Aaron Kline, Emilie Leblanc, Cathy Hou, Cezmi Mutlu, Kaitlyn Dunlap, Yordan Penev, Maya Varma, Nate Stockham, Brianna Chrisman, Kelley Paskov, Min Woo Sun, Jae-Yoon Jung, Catalin Voss, Nick Haber, Dennis P. Wall

The classifier achieved 66. 9% balanced accuracy and 67. 4% F1-score on the entirety of CAFE as well as 79. 1% balanced accuracy and 78. 0% F1-score on CAFE Subset A, a subset containing at least 60% human agreement on emotions labels.

Emotion Classification

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

1 code implementation EMNLP (WNUT) 2020 Rachel Gardner, Maya Varma, Clare Zhu, Ranjay Krishna

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.

Multi-Task Learning

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