
Variational fdivergence Minimization
Probabilistic models are often trained by maximum likelihood, which corr...
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Gaussian mixture models with Wasserstein distance
Generative models with both discrete and continuous latent variables are...
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Private Machine Learning via Randomised Response
We introduce a general learning framework for private machine learning b...
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Thinking Fast and Slow with Deep Learning and Tree Search
Sequential decision making problems, such as structured prediction, robo...
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Practical GaussNewton Optimisation for Deep Learning
We present an efficient blockdiagonal ap proximation to the GaussNewt...
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Nesterov's Accelerated Gradient and Momentum as approximations to Regularised Update Descent
We present a unifying framework for adapting the update direction in gra...
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Dealing with a large number of classes  Likelihood, Discrimination or Ranking?
We consider training probabilistic classifiers in the case of a large nu...
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Efficient Inference in Markov Control Problems
Markov control algorithms that perform smooth, nongreedy updates of the...
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Variational Optimization
We discuss a general technique that can be used to form a differentiable...
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Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning
Long ShortTerm Memory (LSTM) is a popular approach to boosting the abil...
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Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting
We introduce the Kronecker factored online Laplace approximation for ove...
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Improving latent variable descriptiveness with AutoGen
Powerful generative models, particularly in Natural Language Modelling, ...
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Generating Sentences Using a Dynamic Canvas
We introduce the Attentive Unsupervised Text (W)riter (AUTR), which is a...
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Generative Neural Machine Translation
We introduce Generative Neural Machine Translation (GNMT), a latent vari...
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Tracking by Animation: Unsupervised Learning of MultiObject Attentive Trackers
Online MultiObject Tracking (MOT) from videos is a challenging computer...
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Stochastic Variational Optimization
Variational Optimization forms a differentiable upper bound on an object...
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Spread Divergences
For distributions p and q with different support, the divergence general...
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Modular Networks: Learning to Decompose Neural Computation
Scaling model capacity has been vital in the success of deep learning. F...
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Gaussian Mean Field Regularizes by Limiting Learned Information
Variational inference with a factorized Gaussian posterior estimate is a...
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Practical Lossless Compression with Latent Variables using Bits Back Coding
Deep latent variable models have seen recent success in many data domain...
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HiLLoC: Lossless Image Compression with Hierarchical Latent Variable Models
We make the following striking observation: fully convolutional VAE mode...
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David Barber
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