
Interpreting Spatially Infinite Generative Models
Traditional deep generative models of images and other spatial modalitie...
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Diagnostic Questions:The NeurIPS 2020 Education Challenge
Digital technologies are becoming increasingly prevalent in education, e...
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MetaLearning Stationary Stochastic Process Prediction with Convolutional Neural Processes
Stationary stochastic processes (SPs) are a key component of many probab...
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Continual Deep Learning by Functional Regularisation of Memorable Past
Continually learning new skills is important for intelligent systems, ye...
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TaskNorm: Rethinking Batch Normalization for MetaLearning
Modern metalearning approaches for image classification rely on increas...
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Semisupervised Bootstrapping of Dialogue State Trackers for Task Oriented Modelling
Dialogue systems benefit greatly from optimizing on detailed annotations...
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Differentially Private Federated Variational Inference
In many realworld applications of machine learning, data are distribute...
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Continual Learning with Adaptive Weights (CLAW)
Approaches to continual learning aim to successfully learn a set of rela...
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Scalable Exact Inference in MultiOutput Gaussian Processes
Multioutput Gaussian processes (MOGPs) leverage the flexibility and int...
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Convolutional Conditional Neural Processes
We introduce the Convolutional Conditional Neural Process (ConvCNP), a n...
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Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations
Recently there has been an increased interest in unsupervised learning o...
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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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Fast and Flexible MultiTask Classification Using Conditional Neural Adaptive Processes
The goal of this paper is to design image classification systems that, a...
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Practical Deep Learning with Bayesian Principles
Bayesian methods promise to fix many shortcomings of deep learning, but ...
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Fast computation of loudness using a deep neural network
The present paper introduces a deep neural network (DNN) for predicting ...
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Improving and Understanding Variational Continual Learning
In the continual learning setting, tasks are encountered sequentially. T...
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Partitioned Variational Inference: A unified framework encompassing federated and continual learning
Variational inference (VI) has become the method of choice for fitting m...
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InfiniteHorizon Gaussian Processes
Gaussian processes provide a flexible framework for forecasting, removin...
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Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks
Bayesian neural networks (BNNs) hold great promise as a flexible and pri...
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DecisionTheoretic MetaLearning: Versatile and Efficient Amortization of FewShot Learning
This paper develops a general framework for data efficient and versatile...
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Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning
Nonlinear ICA is a fundamental problem for unsupervised representation l...
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Gaussian Process Behaviour in Wide Deep Neural Networks
Whilst deep neural networks have shown great empirical success, there is...
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Structured Evolution with Compact Architectures for Scalable Policy Optimization
We present a new method of blackbox optimization via gradient approximat...
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The Mirage of ActionDependent Baselines in Reinforcement Learning
Policy gradient methods are a widely used class of modelfree reinforcem...
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Learning CausallyGenerated Stationary Time Series
We present the Causal Gaussian Process Convolution Model (CGPCM), a doub...
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The Gaussian Process Autoregressive Regression Model (GPAR)
Multioutput regression models must exploit dependencies between outputs...
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Conditional Density Estimation with Bayesian Normalising Flows
Modeling complex conditional distributions is critical in a variety of s...
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Variational Continual Learning
This paper develops variational continual learning (VCL), a simple but g...
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Interpolated Policy Gradient: Merging OnPolicy and OffPolicy Gradient Estimation for Deep Reinforcement Learning
Offpolicy modelfree deep reinforcement learning methods using previous...
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Discriminative kshot learning using probabilistic models
This paper introduces a probabilistic framework for kshot image classif...
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Streaming Sparse Gaussian Process Approximations
Sparse pseudopoint approximations for Gaussian process (GP) models prov...
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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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Approximate Inference with Amortised MCMC
We propose a novel approximate inference algorithm that approximates a t...
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Sequence Tutor: Conservative FineTuning of Sequence Generation Models with KLcontrol
This paper proposes a general method for improving the structure and qua...
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A Unifying Framework for Gaussian Process PseudoPoint Approximations using Power Expectation Propagation
Gaussian processes (GPs) are flexible distributions over functions that ...
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The Multivariate Generalised von Mises distribution: Inference and applications
Circular variables arise in a multitude of datamodelling contexts rangi...
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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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Denoising without access to clean data using a partitioned autoencoder
Training a denoising autoencoder neural network requires access to truly...
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Stochastic Expectation Propagation
Expectation propagation (EP) is a deterministic approximation algorithm ...
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Neural Adaptive Sequential Monte Carlo
Sequential Monte Carlo (SMC), or particle filtering, is a popular class ...
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On Sparse variational methods and the KullbackLeibler divergence between stochastic processes
The variational framework for learning inducing variables (Titsias, 2009...
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Target Fishing: A SingleLabel or MultiLabel Problem?
According to Cobanoglu et al and Murphy, it is now widely acknowledged t...
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