University of Edinburgh
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Masked Autoregressive Flow for Density Estimation
This work describes an approach for increasing the flexibility of an autoregressive model, based on modelling the random numbers that the model uses internally when generating data, which is called Masked Autoregressive Flow.
MADE: Masked Autoencoder for Distribution Estimation
This work introduces a simple modification for autoencoder neural networks that yields powerful generative models and proves that this approach is competitive with state-of-the-art tractable distribution estimators.
The Neural Autoregressive Distribution Estimator
A new approach for modeling the distribution of high-dimensional vectors of discrete variables inspired by the restricted Boltzmann machine, which outperforms other multivariate binary distribution estimators on several datasets and performs similarly to a large (but intractable) RBM.
On the quantitative analysis of deep belief networks
It is shown that Annealed Importance Sampling (AIS) can be used to efficiently estimate the partition function of an RBM, and a novel AIS scheme for comparing RBM's with different architectures is presented.
MCMC for Doubly-intractable Distributions
This paper provides a generalization of M0ller et al. (2004) and a new MCMC algorithm, which obtains better acceptance probabilities for the same amount of exact sampling, and removes the need to estimate model parameters before sampling begins.
Elliptical slice sampling
A new Markov chain Monte Carlo algorithm for performing inference in models with multivariate Gaussian priors is presented, which has simple, generic code applicable to many models, and works well for a variety of Gaussian process based models.
Evaluation methods for topic models
It is demonstrated experimentally that commonly-used methods are unlikely to accurately estimate the probability of held-out documents, and two alternative methods that are both accurate and efficient are proposed.
Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities
This paper presents the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finitedimensional proxy distributions, and uses a generative model for Poisson data to enable tractable inference via Markov chain Monte Carlo.
Neural Spline Flows
This work proposes a fully-differentiable module based on monotonic rational-quadratic splines, which enhances the flexibility of both coupling and autoregressive transforms while retaining analytic invertibility, and demonstrates that neural spline flows improve density estimation, variational inference, and generative modeling of images.
Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation
This work proposes a new approach to likelihood-free inference based on Bayesian conditional density estimation, which requires fewer model simulations than Monte Carlo ABC methods need to produce a single sample from an approximate posterior.