Y
Yee Whye Teh
Researcher at University of Oxford
Publications - 351
Citations - 42930
Yee Whye Teh is an academic researcher from University of Oxford. The author has contributed to research in topics: Computer science & Inference. The author has an hindex of 68, co-authored 326 publications receiving 36155 citations. Previous affiliations of Yee Whye Teh include University of Toronto & University College London.
Papers
More filters
Posted Content
Mixed Cumulative Distribution Networks
TL;DR: In this paper, the authors apply recent work on cumulative distribution networks and copulas to propose one one general construction for ADMG models, which can succinctly capture much richer sets of conditional independencies, and are especially useful in modeling the effects of latent variables implicitly.
Journal ArticleDOI
A characterization of product-form exchangeable feature probability functions
TL;DR: In this article, the authors characterize the class of exchangeable feature allocations assigning probability Vn,k∏kl=1WmlUn−ml to a feature allocation of n individuals, displaying k features with counts (m1,…,mk) for these features.
Posted Content
Hierarchical Representations with Poincar\'e Variational Auto-Encoders
TL;DR: This work endow VAE with a Poincar\'e ball model of hyperbolic geometry and derive the necessary methods to work with two main Gaussian generalisations on that space.
Posted Content
Collaborative Filtering with Side Information: a Gaussian Process Perspective
TL;DR: The Tucker Gaussian Process is presented, which generalises classical Bayesian matrix factorisation models, and goes beyond them to give a natural and elegant method for incorporating side information, giving enhanced predictive performance for CF problems.
Journal Article
UncertaINR: Uncertainty Quantification of End-to-End Implicit Neural Representations for Computed Tomography
TL;DR: This work studies UncertaINR: a Bayesian reformulation of INR-based image reconstruction, for computed tomography (CT), and finds that it achieves well-calibrated uncertainty, while retaining accuracy competitive with other classical, INRbased, and CNN-based reconstruction techniques.