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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
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Proceedings ArticleDOI
NUS-ML:Improving Word Sense Disambiguation Using Topic Features
TL;DR: A topic feature is constructed, targeted to capture the global context information, using the latent dirichlet allocation (LDA) algorithm with unlabeled corpus, and a modified naive Bayes classifier is constructed to incorporate all the features.
Posted Content
Variational Bayesian Optimal Experimental Design
Adam Foster,Martin Jankowiak,Eli Bingham,Paul Horsfall,Yee Whye Teh,Tom Rainforth,Noah D. Goodman +6 more
TL;DR: This work introduces several classes of fast EIG estimators by building on ideas from amortized variational inference, and shows theoretically and empirically that these estimators can provide significant gains in speed and accuracy over previous approaches.
Proceedings Article
Learning to Parse Images
TL;DR: Using parse trees as internal representations of images, credibility networks are able to perform segmentation and recognition simultaneously, removing the need for ad hoc segmentation heuristics.
Journal ArticleDOI
Bayesian nonparametric crowdsourcing
TL;DR: In this paper, two new fully unsupervised models based on a Chinese restaurant process (CRP) prior and a hierarchical structure that allows inferring these groups jointly with the ground truth and the properties of the users are proposed.
Proceedings Article
Distributed Bayesian Posterior Sampling via Moment Sharing
TL;DR: In this paper, a distributed Markov chain Monte Carlo (MCMC) inference algorithm for large scale Bayesian posterior simulation is proposed, where moment statistics of the local posteriors are collected from each sampler and propagated across the cluster using expectation propagation message passing.