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Language Through a Prism: A Spectral Approach for Multiscale Language Representations
Alex Tamkin, Dan Jurafsky, Noah Goodman
Language exhibits structure at different scales, ranging from subwords to words, sentences, paragraphs, and documents. To what extent do deep models capture information at these sc…
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…
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…
Learning to refer informatively by amortizing pragmatic reasoning
Julia White, Jesse Mu, Noah D. Goodman
A hallmark of human language is the ability to effectively and efficiently convey contextually relevant information. One theory for how humans reason about language is presented in…