
A Study on Efficiency in Continual Learning Inspired by Human Learning
Humans are efficient continual learning systems; we continually learn ne...
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Hierarchical Sparse Variational Autoencoder for Text Encoding
In this paper we focus on unsupervised representation learning and propo...
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Interpreting Spatially Infinite Generative Models
Traditional deep generative models of images and other spatial modalitie...
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MetaLearning for Variational Inference
Variational inference (VI) plays an essential role in approximate Bayesi...
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Sliced Kernelized Stein Discrepancy
Kernelized Stein discrepancy (KSD), though being extensively used in goo...
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A Causal View on Robustness of Neural Networks
We present a causal view on the robustness of neural networks against in...
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Inverse Graphics GAN: Learning to Generate 3D Shapes from Unstructured 2D Data
Recent work has shown the ability to learn generative models for 3D shap...
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Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
The ability for policies to generalize to new environments is key to the...
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On the Importance of the KullbackLeibler Divergence Term in Variational Autoencoders for Text Generation
Variational Autoencoders (VAEs) are known to suffer from learning uninfo...
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Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks
Neural networks provide stateoftheart performance on a variety of tas...
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'InBetween' Uncertainty in Bayesian Neural Networks
We describe a limitation in the expressiveness of the predictive uncerta...
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Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care
Clinical decision making is challenging because of pathological complexi...
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MetaLearning for Stochastic Gradient MCMC
Stochastic gradient Markov chain Monte Carlo (SGMCMC) has become increa...
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Variational Implicit Processes
This paper introduces the variational implicit processes (VIPs), a Bayes...
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A Deep Generative Model for Disentangled Representations of Sequential Data
We present a VAE architecture for encoding and generating high dimension...
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Are Generative Classifiers More Robust to Adversarial Attacks?
There is a rising interest in studying the robustness of deep neural net...
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Variational Continual Learning
This paper develops variational continual learning (VCL), a simple but g...
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Gradient Estimators for Implicit Models
Implicit models, which allow for the generation of samples but not for p...
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Dropout Inference in Bayesian Neural Networks with Alphadivergences
To obtain uncertainty estimates with realworld Bayesian deep learning m...
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Approximate Inference with Amortised MCMC
We propose a novel approximate inference algorithm that approximates a t...
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Deep Gaussian Processes for Regression using Approximate Expectation Propagation
Deep Gaussian processes (DGPs) are multilayer hierarchical generalisati...
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Rényi Divergence Variational Inference
This paper introduces the variational Rényi bound (VR) that extends trad...
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Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation
Deep Gaussian processes (DGPs) are multilayer hierarchical generalisati...
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Stochastic Expectation Propagation for Large Scale Gaussian Process Classification
A method for large scale Gaussian process classification has been recent...
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Blackbox αdivergence Minimization
Blackbox alpha (BBα) is a new approximate inference method based on th...
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Stochastic Expectation Propagation
Expectation propagation (EP) is a deterministic approximation algorithm ...
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