Richard E. Turner

research

∙ 06/20/2022

Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification

Recent years have seen a growth in user-centric applications that requir...

Massimiliano Patacchiola, et al. ∙

research

∙ 06/17/2022

FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification

Modern deep learning systems are increasingly deployed in situations suc...

Aliaksandra Shysheya, et al. ∙

research

∙ 05/18/2022

Multi-disciplinary fairness considerations in machine learning for clinical trials

While interest in the application of machine learning to improve healthc...

Isabel Chien, et al. ∙

research

∙ 03/16/2022

Practical Conditional Neural Processes Via Tractable Dependent Predictions

Conditional Neural Processes (CNPs; Garnelo et al., 2018a) are meta-lear...

Stratis Markou, et al. ∙

research

∙ 03/14/2022

Modelling Non-Smooth Signals with Complex Spectral Structure

The Gaussian Process Convolution Model (GPCM; Tobar et al., 2015a) is a ...

Wessel P. Bruinsma, et al. ∙

research

∙ 02/24/2022

Partitioned Variational Inference: A Framework for Probabilistic Federated Learning

The proliferation of computing devices has brought about an opportunity ...

Matthew Ashman, et al. ∙

research

∙ 07/02/2021

Memory Efficient Meta-Learning with Large Images

Meta learning approaches to few-shot classification are computationally ...

John Bronskill, et al. ∙

research

∙ 06/24/2021

Continual Novelty Detection

Novelty Detection methods identify samples that are not representative o...

Rahaf Aljundi, et al. ∙

research

∙ 06/18/2021

Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes

Gaussian processes (GPs) are important probabilistic tools for inference...

Will Tebbutt, et al. ∙

research

∙ 06/07/2021

How Tight Can PAC-Bayes be in the Small Data Regime?

In this paper, we investigate the question: Given a small number of data...

Andrew Y. K. Foong, et al. ∙

research

∙ 01/20/2021

Convolutional conditional neural processes for local climate downscaling

A new model is presented for multisite statistical downscaling of temper...

Anna Vaughan, et al. ∙

research

∙ 01/10/2021

The Gaussian Neural Process

Neural Processes (NPs; Garnelo et al., 2018a,b) are a rich class of mode...

Wessel P. Bruinsma, et al. ∙

research

∙ 11/24/2020

Generalized Variational Continual Learning

Continual learning deals with training models on new tasks and datasets ...

Noel Loo, et al. ∙

research

∙ 10/20/2020

Sparse Gaussian Process Variational Autoencoders

Large, multi-dimensional spatio-temporal datasets are omnipresent in mod...

Matthew Ashman, et al. ∙

research

∙ 07/24/2020

Interpreting Spatially Infinite Generative Models

Traditional deep generative models of images and other spatial modalitie...

Chaochao Lu, et al. ∙

research

∙ 07/23/2020

Diagnostic Questions:The NeurIPS 2020 Education Challenge

Digital technologies are becoming increasingly prevalent in education, e...

Zichao Wang, et al. ∙

research

∙ 07/02/2020

Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes

Stationary stochastic processes (SPs) are a key component of many probab...

Andrew Y. K. Foong, et al. ∙

research

∙ 04/29/2020

Continual Deep Learning by Functional Regularisation of Memorable Past

Continually learning new skills is important for intelligent systems, ye...

Pingbo Pan, et al. ∙

research

∙ 03/06/2020

TaskNorm: Rethinking Batch Normalization for Meta-Learning

Modern meta-learning approaches for image classification rely on increas...

John Bronskill, et al. ∙

research

∙ 11/26/2019

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

Dialogue systems benefit greatly from optimizing on detailed annotations...

Bo-Hsiang Tseng, et al. ∙

research

∙ 11/24/2019

Differentially Private Federated Variational Inference

In many real-world applications of machine learning, data are distribute...

Mrinank Sharma, et al. ∙

research

∙ 11/21/2019

Continual Learning with Adaptive Weights (CLAW)

Approaches to continual learning aim to successfully learn a set of rela...

Tameem Adel, et al. ∙

research

∙ 11/14/2019

Scalable Exact Inference in Multi-Output Gaussian Processes

Multi-output Gaussian processes (MOGPs) leverage the flexibility and int...

Wessel P. Bruinsma, et al. ∙

research

∙ 10/29/2019

Convolutional Conditional Neural Processes

We introduce the Convolutional Conditional Neural Process (ConvCNP), a n...

Jonathan Gordon, et al. ∙

research

∙ 09/05/2019

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations

Recently there has been an increased interest in unsupervised learning o...

Jan Stühmer, et al. ∙

research

∙ 09/02/2019

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...

Andrew Y. K. Foong, et al. ∙

research

∙ 06/27/2019

'In-Between' Uncertainty in Bayesian Neural Networks

We describe a limitation in the expressiveness of the predictive uncerta...

Andrew Y. K. Foong, et al. ∙

research

∙ 06/18/2019

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

The goal of this paper is to design image classification systems that, a...

James Requeima, et al. ∙

research

∙ 06/06/2019

Practical Deep Learning with Bayesian Principles

Bayesian methods promise to fix many shortcomings of deep learning, but ...

Kazuki Osawa, et al. ∙

research

∙ 05/24/2019

Fast computation of loudness using a deep neural network

The present paper introduces a deep neural network (DNN) for predicting ...

Josef Schlittenlacher, et al. ∙

research

∙ 05/06/2019

Improving and Understanding Variational Continual Learning

In the continual learning setting, tasks are encountered sequentially. T...

Siddharth Swaroop, et al. ∙

research

∙ 11/27/2018

Partitioned Variational Inference: A unified framework encompassing federated and continual learning

Variational inference (VI) has become the method of choice for fitting m...

Thang D. Bui, et al. ∙

research

∙ 11/15/2018

Infinite-Horizon Gaussian Processes

Gaussian processes provide a flexible framework for forecasting, removin...

Arno Solin, et al. ∙

research

∙ 10/09/2018

Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks

Bayesian neural networks (BNNs) hold great promise as a flexible and pri...

Anqi Wu, et al. ∙

research

∙ 05/24/2018

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

This paper develops a general framework for data efficient and versatile...

Jonathan Gordon, et al. ∙

research

∙ 05/22/2018

Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning

Nonlinear ICA is a fundamental problem for unsupervised representation l...

Aapo Hyvärinen, et al. ∙

research

∙ 04/30/2018

Gaussian Process Behaviour in Wide Deep Neural Networks

Whilst deep neural networks have shown great empirical success, there is...

Alexander G. de G. Matthews, et al. ∙

research

∙ 04/06/2018

Structured Evolution with Compact Architectures for Scalable Policy Optimization

We present a new method of blackbox optimization via gradient approximat...

Krzysztof Choromanski, et al. ∙

research

∙ 02/27/2018

The Mirage of Action-Dependent Baselines in Reinforcement Learning

Policy gradient methods are a widely used class of model-free reinforcem...

George Tucker, et al. ∙

research

∙ 02/22/2018

Learning Causally-Generated Stationary Time Series

We present the Causal Gaussian Process Convolution Model (CGPCM), a doub...

Wessel Bruinsma, et al. ∙

research

∙ 02/20/2018

The Gaussian Process Autoregressive Regression Model (GPAR)

Multi-output regression models must exploit dependencies between outputs...

James Requeima, et al. ∙

research

∙ 02/14/2018

Conditional Density Estimation with Bayesian Normalising Flows

Modeling complex conditional distributions is critical in a variety of s...

Brian L Trippe, et al. ∙

research

∙ 10/29/2017

Variational Continual Learning

This paper develops variational continual learning (VCL), a simple but g...

Cuong V. Nguyen, et al. ∙

research

∙ 06/01/2017

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...

Shixiang Gu, et al. ∙

research

∙ 06/01/2017

Discriminative k-shot learning using probabilistic models

This paper introduces a probabilistic framework for k-shot image classif...

Matthias Bauer, et al. ∙

research

∙ 05/19/2017

Streaming Sparse Gaussian Process Approximations

Sparse pseudo-point approximations for Gaussian process (GP) models prov...

Thang D. Bui, et al. ∙

research

∙ 05/19/2017

Gradient Estimators for Implicit Models

Implicit models, which allow for the generation of samples but not for p...

Yingzhen Li, et al. ∙

research

∙ 02/27/2017

Approximate Inference with Amortised MCMC

We propose a novel approximate inference algorithm that approximates a t...

Yingzhen Li, et al. ∙

research

∙ 11/09/2016

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

This paper proposes a general method for improving the structure and qua...

Natasha Jaques, et al. ∙

research

∙ 05/23/2016

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

Gaussian processes (GPs) are flexible distributions over functions that ...

Thang D. Bui, et al. ∙

Richard E. Turner

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