3 citations · 5 across the 12 of their papers we have counts for
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cs.CV2022
Improving self-supervised representation learning via sequential adversarial masking
Dylan Sam, Min Bai, Tristan McKinney +1
Recent methods in self-supervised learning have demonstrated that masking-based pretext tasks extend beyond NLP, serving as useful pretraining objectives in computer vision. Howeve…
cs.LG2022
Losses over Labels: Weakly Supervised Learning via Direct Loss Construction
Dylan Sam, J. Zico Kolter
Owing to the prohibitive costs of generating large amounts of labeled data, programmatic weak supervision is a growing paradigm within machine learning. In this setting, users desi…
cs.LG2022
Label Propagation with Weak Supervision
Rattana Pukdee, Dylan Sam, Maria-Florina Balcan +1
Semi-supervised learning and weakly supervised learning are important paradigms that aim to reduce the growing demand for labeled data in current machine learning applications. In…