104 citations · 209 across the 10 of their papers we have counts for
6 papers · 1 filter
A Simple Framework for Uncertainty in Contrastive Learning
Mike Wu, Noah Goodman
Contrastive approaches to representation learning have recently shown great promise. In contrast to generative approaches, these contrastive models learn a deterministic encoder wi…
Conditional Negative Sampling for Contrastive Learning of Visual Representations
Mike Wu, Milan Mosse, Chengxu Zhuang +2
Recent methods for learning unsupervised visual representations, dubbed contrastive learning, optimize the noise-contrastive estimation (NCE) bound on mutual information between tw…
HarperValleyBank: A Domain-Specific Spoken Dialog Corpus
Mike Wu, Jonathan Nafziger, Anthony Scodary +1
We introduce HarperValleyBank, a free, public domain spoken dialog corpus. The data simulate simple consumer banking interactions, containing about 23 hours of audio from 1,446 hum…
Viewmaker Networks: Learning Views for Unsupervised Representation Learning
Alex Tamkin, Mike Wu, Noah Goodman
Many recent methods for unsupervised representation learning train models to be invariant to different "views," or distorted versions of an input. However, designing these views re…
On Mutual Information in Contrastive Learning for Visual Representations
Mike Wu, Chengxu Zhuang, Milan Mosse +2
In recent years, several unsupervised, "contrastive" learning algorithms in vision have been shown to learn representations that perform remarkably well on transfer tasks. We show…
Variational Item Response Theory: Fast, Accurate, and Expressive
Mike Wu, Richard L. Davis, Benjamin W. Domingue +2
Item Response Theory (IRT) is a ubiquitous model for understanding humans based on their responses to questions, used in fields as diverse as education, medicine and psychology. La…