5 citations · 10 across the 12 of their papers we have counts for
5 papers · 1 filter
Improving Domain Generalization in Contrastive Learning using Adaptive Temperature Control
Robert Lewis, Katie Matton, Rosalind W. Picard +1
Self-supervised pre-training with contrastive learning is a powerful method for learning from sparsely labeled data. However, performance can drop considerably when there is a shif…
Test-time augmentation improves efficiency in conformal prediction
Divya Shanmugam, Helen Lu, Swami Sankaranarayanan +1
A conformal classifier produces a set of predicted classes and provides a probabilistic guarantee that the set includes the true class. Unfortunately, it is often the case that con…
Evaluating multiple models using labeled and unlabeled data
Divya Shanmugam, Shuvom Sadhuka, Manish Raghavan +3
It remains difficult to evaluate machine learning classifiers in the absence of a large, labeled dataset. While labeled data can be prohibitively expensive or impossible to obtain,…
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…
Multi-Similarity Contrastive Learning
Emily Mu, John Guttag, Maggie Makar
Given a similarity metric, contrastive methods learn a representation in which examples that are similar are pushed together and examples that are dissimilar are pulled apart. Cont…