12 citations · 19 across the 3 of their papers we have counts for
5 papers
A simple, efficient and scalable contrastive masked autoencoder for learning visual representations
Shlok Mishra, Joshua Robinson, Huiwen Chang +4
We introduce CAN, a simple, efficient and scalable method for self-supervised learning of visual representations. Our framework is a minimal and conceptually clean synthesis of (C)…
Contrastive Learning with Hard Negative Samples
Joshua Robinson, Ching-Yao Chuang, Suvrit Sra +1
How can you sample good negative examples for contrastive learning? We argue that, as with metric learning, contrastive learning of representations benefits from hard negative samp…
Debiased Contrastive Learning
Ching-Yao Chuang, Joshua Robinson, Lin Yen-Chen +2
A prominent technique for self-supervised representation learning has been to contrast semantically similar and dissimilar pairs of samples. Without access to labels, dissimilar (n…
Strength from Weakness: Fast Learning Using Weak Supervision
Joshua Robinson, Stefanie Jegelka, Suvrit Sra
We study generalization properties of weakly supervised learning. That is, learning where only a few "strong" labels (the actual target of our prediction) are present but many more…
Flexible Modeling of Diversity with Strongly Log-Concave Distributions
Joshua Robinson, Suvrit Sra, Stefanie Jegelka
Strongly log-concave (SLC) distributions are a rich class of discrete probability distributions over subsets of some ground set. They are strictly more general than strongly Raylei…