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A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
Yarin Gal, Zoubin Ghahramani
Recurrent neural networks (RNNs) stand at the forefront of many recent developments in deep learning. Yet a major difficulty with these models is their tendency to overfit, with dr…
Dirichlet Fragmentation Processes
Hong Ge, Yarin Gal, Zoubin Ghahramani
Tree structures are ubiquitous in data across many domains, and many datasets are naturally modelled by unobserved tree structures. In this paper, first we review the theory of ran…
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
Yarin Gal, Zoubin Ghahramani
Convolutional neural networks (CNNs) work well on large datasets. But labelled data is hard to collect, and in some applications larger amounts of data are not available. The probl…
Dropout as a Bayesian Approximation: Appendix
Yarin Gal, Zoubin Ghahramani
We show that a neural network with arbitrary depth and non-linearities, with dropout applied before every weight layer, is mathematically equivalent to an approximation to a well k…
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Yarin Gal, Zoubin Ghahramani
Deep learning tools have gained tremendous attention in applied machine learning. However such tools for regression and classification do not capture model uncertainty. In comparis…
Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs
Yarin Gal, Richard Turner
Standard sparse pseudo-input approximations to the Gaussian process (GP) cannot handle complex functions well. Sparse spectrum alternatives attempt to answer this but are known to…
Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data
Yarin Gal, Yutian Chen, Zoubin Ghahramani
Multivariate categorical data occur in many applications of machine learning. One of the main difficulties with these vectors of categorical variables is sparsity. The number of po…