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- Geoffrey E. Hinton, Simon Osindero, Yee Whye Teh
- Neural Computation
- 2006

We show how to use complementary priors to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementaryâ€¦ (More)

We consider problems involving groups of data where each observation within a group is a draw from a mixture model and where it is desirable to share mixture components between groups. We assume thatâ€¦ (More)

- Max Welling, Yee Whye Teh
- ICML
- 2011

In this paper we propose a new framework for learning from large scale datasets based on iterative learning from small mini-batches. By adding the right amount of noise to a standard stochasticâ€¦ (More)

- Yee Whye Teh
- ACL
- 2006

We propose a new hierarchical Bayesian n-gram model of natural languages. Our model makes use of a generalization of the commonly used Dirichlet distributions called Pitman-Yor processes whichâ€¦ (More)

- Arthur U. Asuncion, Max Welling, Padhraic Smyth, Yee Whye Teh
- ArXiv
- 2009

Latent Dirichlet analysis, or topic modeling, is a flexible latent variable framework for modeling high-dimensional sparse count data. Various learning algorithms have been developed in recent years,â€¦ (More)

- Yee Whye Teh, David Newman, Max Welling
- NIPS
- 2006

Latent Dirichlet allocation (LDA) is a Bayesian network tha has recently gained much popularity in applications ranging from document mode ling to computer vision. Due to the large scale nature ofâ€¦ (More)

- Andriy Mnih, Yee Whye Teh
- ICML
- 2012

â€¢ In spite of their superior performance, neural probabilistic language models (NPLMs) are far less widely used than n-gram models due to their notoriously long training times. â€¢We introduce a simpleâ€¦ (More)

- Yee Whye Teh, Dilan GÃ¶rÃ¼r, Zoubin Ghahramani
- AISTATS
- 2007

The Indian buffet process (IBP) is a Bayesian nonparametric distribution whereby objects are modelled using an unbounded number of latent features. In this paper we derive a stick-breakingâ€¦ (More)

- Yee Whye Teh
- Encyclopedia of Machine Learning
- 2010

The Dirichlet process is a stochastic proces used in Bayesian nonparametric models of data, particularly in Dirichlet process mixture models (also known as infinite mixture models). It is aâ€¦ (More)

- Tamara L. Berg, Alexander C. Berg, +5 authors David A. Forsyth
- Proceedings of the 2004 IEEE Computer Societyâ€¦
- 2004

We show quite good face clustering is possible for a dataset of inaccurately and ambiguously labelled face images. Our dataset is 44,773 face images, obtained by applying a face finder toâ€¦ (More)