6 citations · 9 across the 4 of their papers we have counts for
6 papers
GIST: Generating Image-Specific Text for Fine-grained Object Classification
Kathleen M. Lewis, Emily Mu, Adrian V. Dalca +1
Recent vision-language models outperform vision-only models on many image classification tasks. However, because of the absence of paired text/image descriptions, it remains diffic…
Sequential Multi-Dimensional Self-Supervised Learning for Clinical Time Series
Aniruddh Raghu, Payal Chandak, Ridwan Alam +2
Self-supervised learning (SSL) for clinical time series data has received significant attention in recent literature, since these data are highly rich and provide important informa…
UniverSeg: Universal Medical Image Segmentation
Victor Ion Butoi, Jose Javier Gonzalez Ortiz, Tianyu Ma +3
While deep learning models have become the predominant method for medical image segmentation, they are typically not capable of generalizing to unseen segmentation tasks involving…
At the Intersection of Deep Learning and Conceptual Art: The End of Signature
Divya Shanmugam, Katie Lewis, Jose Javier Gonzalez-Ortiz +2
MIT wanted to commission a large scale artwork that would serve to 'illuminate a new campus gateway, inaugurate a space of exchange between MIT and Cambridge, and inspire our stude…
Improved Text Classification via Test-Time Augmentation
Helen Lu, Divya Shanmugam, Harini Suresh +1
Test-time augmentation -- the aggregation of predictions across transformed examples of test inputs -- is an established technique to improve the performance of image classificatio…
Uncovering Voice Misuse Using Symbolic Mismatch
Marzyeh Ghassemi, Zeeshan Syed, Daryush D. Mehta +3
Voice disorders affect an estimated 14 million working-aged Americans, and many more worldwide. We present the first large scale study of vocal misuse based on long-term ambulatory…