
Resampled Priors for Variational Autoencoders
We propose Learned Accept/Reject Sampling (LARS), a method for construct...
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Attentive Neural Processes
Neural Processes (NPs) (Garnelo et al 2018a;b) approach regression by le...
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Monte Carlo Gradient Estimation in Machine Learning
This paper is a broad and accessible survey of the methods we have at ou...
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MuProp: Unbiased Backpropagation for Stochastic Neural Networks
Deep neural networks are powerful parametric models that can be trained ...
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Filtering Variational Objectives
When used as a surrogate objective for maximum likelihood estimation in ...
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REBAR: Lowvariance, unbiased gradient estimates for discrete latent variable models
Learning in models with discrete latent variables is challenging due to ...
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The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
The reparameterization trick enables optimizing large scale stochastic c...
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Variational inference for Monte Carlo objectives
Recent progress in deep latent variable models has largely been driven b...
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Neural Variational Inference and Learning in Belief Networks
Highly expressive directed latent variable models, such as sigmoid belie...
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Learning Item Trees for Probabilistic Modelling of Implicit Feedback
User preferences for items can be inferred from either explicit feedback...
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Disentangling by Factorising
We define and address the problem of unsupervised learning of disentangl...
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Implicit Reparameterization Gradients
By providing a simple and efficient way of computing lowvariance gradie...
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Sparse Orthogonal Variational Inference for Gaussian Processes
We introduce a new interpretation of sparse variational approximations f...
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QLearning in enormous action spaces via amortized approximate maximization
Applying Qlearning to highdimensional or continuous action spaces can ...
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Andriy Mnih
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