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Markov Chain Monte Carlo (MCMC) algorithms are routinely used to draw samples from distributions with intractable normalization constants. However, standard MCMC algorithms do not apply to… (More)

- Ruslan Salakhutdinov, Iain Murray
- ICML
- 2008

Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allowed these models to be… (More)

- Iain Murray, Ryan P. Adams, David J. C. MacKay
- AISTATS
- 2009

Many probabilistic models introduce strong dependencies between variables using a latent multivariate Gaussian distribution or a Gaussian process. We present a new Markov chain Monte Carlo algorithm… (More)

- Hugo Larochelle, Iain Murray
- AISTATS
- 2011

We describe a new approach for modeling the distribution of high-dimensional vectors of discrete variables. This model is inspired by the restricted Boltzmann machine (RBM), which has been shown to… (More)

A natural evaluation metric for statistical topic models is the probability of held-out documents given a trained model. While exact computation of this probability is intractable, several estimators… (More)

- George Papamakarios, Theo Pavlakou, Iain Murray
- NIPS
- 2017

Autoregressive models are among the best performing neural density estimators. We describe an approach for increasing the flexibility of an autoregressive model, based on modelling the random numbers… (More)

- Mathieu Germain, Karol Gregor, Iain Murray, Hugo Larochelle
- ICML
- 2015

There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples. We introduce a simple modification for autoencoder neural networks that… (More)

- Iain Murray, Ryan P. Adams
- NIPS
- 2010

The Gaussian process (GP) is a popular way to specify dependencies between random variables in a probabilistic model. In the Bayesian framework the covariance structure can be specified using unknown… (More)

- Iain Murray, Zoubin Ghahramani
- UAI
- 2004

Bayesian learning in undirected graphical models---computing posterior distributions over parameters and predictive quantities---is exceptionally difficult. We conjecture that for general undirected… (More)

- Ben Krause, Emmanuel Kahembwe, Iain Murray, Steve Renals
- ICML
- 2017

We present methodology for using dynamic evaluation to improve neural sequence models. Models are adapted to recent history via a gradient descent based mechanism, causing them to assign higher… (More)