Richard E. Turner

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University of Cambridge

University Lecturer, University of Cambridge

Interpreting Spatially Infinite Generative Models

Traditional deep generative models of images and other spatial modalitie...
07/24/2020 ∙ by Chaochao Lu, et al. ∙ 6 ∙ share

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Diagnostic Questions:The NeurIPS 2020 Education Challenge

Digital technologies are becoming increasingly prevalent in education, e...
07/23/2020 ∙ by Zichao Wang, et al. ∙ 0 ∙ share

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Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes

Stationary stochastic processes (SPs) are a key component of many probab...
07/02/2020 ∙ by Andrew Y. K. Foong, et al. ∙ 2 ∙ share

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Continual Deep Learning by Functional Regularisation of Memorable Past

Continually learning new skills is important for intelligent systems, ye...
04/29/2020 ∙ by Pingbo Pan, et al. ∙ 17 ∙ share

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TaskNorm: Rethinking Batch Normalization for Meta-Learning

Modern meta-learning approaches for image classification rely on increas...
03/06/2020 ∙ by John Bronskill, et al. ∙ 12 ∙ share

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Semi-supervised Bootstrapping of Dialogue State Trackers for Task Oriented Modelling

Dialogue systems benefit greatly from optimizing on detailed annotations...
11/26/2019 ∙ by Bo-Hsiang Tseng, et al. ∙ 0 ∙ share

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Differentially Private Federated Variational Inference

In many real-world applications of machine learning, data are distribute...
11/24/2019 ∙ by Mrinank Sharma, et al. ∙ 16 ∙ share

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Continual Learning with Adaptive Weights (CLAW)

Approaches to continual learning aim to successfully learn a set of rela...
11/21/2019 ∙ by Tameem Adel, et al. ∙ 20 ∙ share

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Scalable Exact Inference in Multi-Output Gaussian Processes

Multi-output Gaussian processes (MOGPs) leverage the flexibility and int...
11/14/2019 ∙ by Wessel P. Bruinsma, et al. ∙ 15 ∙ share

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Convolutional Conditional Neural Processes

We introduce the Convolutional Conditional Neural Process (ConvCNP), a n...
10/29/2019 ∙ by Jonathan Gordon, et al. ∙ 45 ∙ share

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Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations

Recently there has been an increased interest in unsupervised learning o...
09/05/2019 ∙ by Jan Stühmer, et al. ∙ 24 ∙ share

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Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks

Neural networks provide state-of-the-art performance on a variety of tas...
09/02/2019 ∙ by Andrew Y. K. Foong, et al. ∙ 5 ∙ share

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'In-Between' Uncertainty in Bayesian Neural Networks

We describe a limitation in the expressiveness of the predictive uncerta...
06/27/2019 ∙ by Andrew Y. K. Foong, et al. ∙ 1 ∙ share

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Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes

The goal of this paper is to design image classification systems that, a...
06/18/2019 ∙ by James Requeima, et al. ∙ 6 ∙ share

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Practical Deep Learning with Bayesian Principles

Bayesian methods promise to fix many shortcomings of deep learning, but ...
06/06/2019 ∙ by Kazuki Osawa, et al. ∙ 23 ∙ share

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Fast computation of loudness using a deep neural network

The present paper introduces a deep neural network (DNN) for predicting ...
05/24/2019 ∙ by Josef Schlittenlacher, et al. ∙ 0 ∙ share

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Improving and Understanding Variational Continual Learning

In the continual learning setting, tasks are encountered sequentially. T...
05/06/2019 ∙ by Siddharth Swaroop, et al. ∙ 12 ∙ share

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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...
11/27/2018 ∙ by Thang D. Bui, et al. ∙ 0 ∙ share

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Infinite-Horizon Gaussian Processes

Gaussian processes provide a flexible framework for forecasting, removin...
11/15/2018 ∙ by Arno Solin, et al. ∙ 8 ∙ share

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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...
10/09/2018 ∙ by Anqi Wu, et al. ∙ 6 ∙ share

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Decision-Theoretic Meta-Learning: Versatile and Efficient Amortization of Few-Shot Learning

This paper develops a general framework for data efficient and versatile...
05/24/2018 ∙ by Jonathan Gordon, et al. ∙ 0 ∙ share

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Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning

Nonlinear ICA is a fundamental problem for unsupervised representation l...
05/22/2018 ∙ by Aapo Hyvärinen, et al. ∙ 0 ∙ share

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Gaussian Process Behaviour in Wide Deep Neural Networks

Whilst deep neural networks have shown great empirical success, there is...
04/30/2018 ∙ by Alexander G. de G. Matthews, et al. ∙ 0 ∙ share

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Structured Evolution with Compact Architectures for Scalable Policy Optimization

We present a new method of blackbox optimization via gradient approximat...
04/06/2018 ∙ by Krzysztof Choromanski, et al. ∙ 0 ∙ share

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The Mirage of Action-Dependent Baselines in Reinforcement Learning

Policy gradient methods are a widely used class of model-free reinforcem...
02/27/2018 ∙ by George Tucker, et al. ∙ 0 ∙ share

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Learning Causally-Generated Stationary Time Series

We present the Causal Gaussian Process Convolution Model (CGPCM), a doub...
02/22/2018 ∙ by Wessel Bruinsma, et al. ∙ 0 ∙ share

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The Gaussian Process Autoregressive Regression Model (GPAR)

Multi-output regression models must exploit dependencies between outputs...
02/20/2018 ∙ by James Requeima, et al. ∙ 0 ∙ share

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Conditional Density Estimation with Bayesian Normalising Flows

Modeling complex conditional distributions is critical in a variety of s...
02/14/2018 ∙ by Brian L Trippe, et al. ∙ 0 ∙ share

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Variational Continual Learning

This paper develops variational continual learning (VCL), a simple but g...
10/29/2017 ∙ by Cuong V. Nguyen, et al. ∙ 0 ∙ share

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Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning

Off-policy model-free deep reinforcement learning methods using previous...
06/01/2017 ∙ by Shixiang Gu, et al. ∙ 0 ∙ share

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Discriminative k-shot learning using probabilistic models

This paper introduces a probabilistic framework for k-shot image classif...
06/01/2017 ∙ by Matthias Bauer, et al. ∙ 0 ∙ share

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Streaming Sparse Gaussian Process Approximations

Sparse pseudo-point approximations for Gaussian process (GP) models prov...
05/19/2017 ∙ by Thang D. Bui, et al. ∙ 0 ∙ share

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Gradient Estimators for Implicit Models

Implicit models, which allow for the generation of samples but not for p...
05/19/2017 ∙ by Yingzhen Li, et al. ∙ 0 ∙ share

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Approximate Inference with Amortised MCMC

We propose a novel approximate inference algorithm that approximates a t...
02/27/2017 ∙ by Yingzhen Li, et al. ∙ 0 ∙ share

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Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control

This paper proposes a general method for improving the structure and qua...
11/09/2016 ∙ by Natasha Jaques, et al. ∙ 0 ∙ share

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A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation

Gaussian processes (GPs) are flexible distributions over functions that ...
05/23/2016 ∙ by Thang D. Bui, et al. ∙ 0 ∙ share

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The Multivariate Generalised von Mises distribution: Inference and applications

Circular variables arise in a multitude of data-modelling contexts rangi...
02/16/2016 ∙ by Alexandre K. W. Navarro, et al. ∙ 0 ∙ share

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Deep Gaussian Processes for Regression using Approximate Expectation Propagation

Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisati...
02/12/2016 ∙ by Thang D. Bui, et al. ∙ 0 ∙ share

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Rényi Divergence Variational Inference

This paper introduces the variational Rényi bound (VR) that extends trad...
02/06/2016 ∙ by Yingzhen Li, et al. ∙ 0 ∙ share

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Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation

Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisati...
11/11/2015 ∙ by Thang D. Bui, et al. ∙ 0 ∙ share

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Stochastic Expectation Propagation for Large Scale Gaussian Process Classification

A method for large scale Gaussian process classification has been recent...
11/10/2015 ∙ by Daniel Hernández-Lobato, et al. ∙ 0 ∙ share

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Black-box α-divergence Minimization

Black-box alpha (BB-α) is a new approximate inference method based on th...
11/10/2015 ∙ by José Miguel Hernández-Lobato, et al. ∙ 0 ∙ share

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Denoising without access to clean data using a partitioned autoencoder

Training a denoising autoencoder neural network requires access to truly...
09/20/2015 ∙ by Dan Stowell, et al. ∙ 0 ∙ share

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Stochastic Expectation Propagation

Expectation propagation (EP) is a deterministic approximation algorithm ...
06/12/2015 ∙ by Yingzhen Li, et al. ∙ 0 ∙ share

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Neural Adaptive Sequential Monte Carlo

Sequential Monte Carlo (SMC), or particle filtering, is a popular class ...
06/10/2015 ∙ by Shixiang Gu, et al. ∙ 0 ∙ share

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On Sparse variational methods and the Kullback-Leibler divergence between stochastic processes

The variational framework for learning inducing variables (Titsias, 2009...
04/27/2015 ∙ by Alexander G. de G. Matthews, et al. ∙ 0 ∙ share

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Target Fishing: A Single-Label or Multi-Label Problem?

According to Cobanoglu et al and Murphy, it is now widely acknowledged t...
11/23/2014 ∙ by Avid M. Afzal, et al. ∙ 0 ∙ share

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Richard E. Turner

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