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Subhashini Venugopalan
Researcher at Google
Publications - 62
Citations - 19468
Subhashini Venugopalan is an academic researcher from Google. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 22, co-authored 50 publications receiving 15683 citations. Previous affiliations of Subhashini Venugopalan include IBM & University of Texas at Austin.
Papers
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Journal ArticleDOI
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
Varun Gulshan,Lily Peng,Marc Coram,Martin C. Stumpe,Derek Wu,Arunachalam Narayanaswamy,Subhashini Venugopalan,Kasumi Widner,Tom Madams,Jorge Cuadros,Ramasamy Kim,Rajiv Raman,Philip C. Nelson,Jessica L. Mega,Dale R. Webster +14 more
TL;DR: An algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy and diabetic macular edema in retinal fundus photographs from adults with diabetes.
Proceedings ArticleDOI
Long-term recurrent convolutional networks for visual recognition and description
Jeff Donahue,Lisa Anne Hendricks,Sergio Guadarrama,Marcus Rohrbach,Subhashini Venugopalan,Trevor Darrell,Kate Saenko +6 more
TL;DR: A novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and shows such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined and/or optimized.
Posted Content
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
Jeff Donahue,Lisa Anne Hendricks,Marcus Rohrbach,Subhashini Venugopalan,Sergio Guadarrama,Kate Saenko,Trevor Darrell +6 more
TL;DR: A novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and shows such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined and/or optimized.
Proceedings ArticleDOI
Sequence to Sequence -- Video to Text
Subhashini Venugopalan,Marcus Rohrbach,Jeff Donahue,Raymond J. Mooney,Trevor Darrell,Kate Saenko +5 more
TL;DR: In this article, an end-to-end sequence to sequence model was proposed to generate captions for videos, which can learn the temporal structure of the sequence of frames as well as the sequence model of the generated sentences, i.e. a language model.
Journal ArticleDOI
Long-Term Recurrent Convolutional Networks for Visual Recognition and Description
Jeff Donahue,Lisa Anne Hendricks,Marcus Rohrbach,Subhashini Venugopalan,Sergio Guadarrama,Kate Saenko,Trevor Darrell +6 more
TL;DR: In this article, a class of recurrent convolutional architectures was proposed for large-scale visual understanding tasks, and demonstrated the value of these models for activity recognition, image captioning, and video description.