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Memory Efficient Meta-Learning with Large Images


Jul 02, 2021
John Bronskill, Daniela Massiceti, Massimiliano Patacchiola, Katja Hofmann, Sebastian Nowozin, Richard E. Turner


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Continual Novelty Detection


Jun 24, 2021
Rahaf Aljundi, Daniel Olmeda Reino, Nikolay Chumerin, Richard E. Turner


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


Jun 18, 2021
Will Tebbutt, Arno Solin, Richard E. Turner


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


Jun 07, 2021
Andrew Y. K. Foong, Wessel P. Bruinsma, David R. Burt, Richard E. Turner

* Preprint. Under review 

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Contextual HyperNetworks for Novel Feature Adaptation


Apr 12, 2021
Angus Lamb, Evgeny Saveliev, Yingzhen Li, Sebastian Tschiatschek, Camilla Longden, Simon Woodhead, José Miguel Hernández-Lobato, Richard E. Turner, Pashmina Cameron, Cheng Zhang

* 17 pages, 9 Figures, workshop paper at NeurIPS 2020 Meta-Learning Workshop 

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Convolutional conditional neural processes for local climate downscaling


Jan 20, 2021
Anna Vaughan, Will Tebbutt, J. Scott Hosking, Richard E. Turner

* 26 pages, 12 figures 

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The Gaussian Neural Process


Jan 10, 2021
Wessel P. Bruinsma, James Requeima, Andrew Y. K. Foong, Jonathan Gordon, Richard E. Turner

* 34 pages; includes supplementary material; to appear in AABI 2020 

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Generalized Variational Continual Learning


Nov 24, 2020
Noel Loo, Siddharth Swaroop, Richard E. Turner


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Sparse Gaussian Process Variational Autoencoders


Oct 23, 2020
Matthew Ashman, Jonathan So, Will Tebbutt, Vincent Fortuin, Michael Pearce, Richard E. Turner

* 19 pages, 6 figures 

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Diagnostic Questions:The NeurIPS 2020 Education Challenge


Aug 03, 2020
Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, José Miguel Hernández-Lobato, Richard E. Turner, Richard G. Baraniuk, Craig Barton, Simon Peyton Jones, Simon Woodhead, Cheng Zhang

* 28 pages, 6 figures, NeurIPS 2020 Competition Track 

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Interpreting Spatially Infinite Generative Models


Jul 24, 2020
Chaochao Lu, Richard E. Turner, Yingzhen Li, Nate Kushman

* ICML 2020 workshop on Human Interpretability in Machine Learning (WHI 2020) 

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Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes


Jul 02, 2020
Andrew Y. K. Foong, Wessel P. Bruinsma, Jonathan Gordon, Yann Dubois, James Requeima, Richard E. Turner


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Continual Deep Learning by Functional Regularisation of Memorable Past


Apr 29, 2020
Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan


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TaskNorm: Rethinking Batch Normalization for Meta-Learning


Mar 06, 2020
John Bronskill, Jonathan Gordon, James Requeima, Sebastian Nowozin, Richard E. Turner


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


Nov 26, 2019
Bo-Hsiang Tseng, Marek Rei, Paweł Budzianowski, Richard E. Turner, Bill Byrne, Anna Korhonen

* This article is published at EMNLP-IJCNLP 2019 

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Differentially Private Federated Variational Inference


Nov 24, 2019
Mrinank Sharma, Michael Hutchinson, Siddharth Swaroop, Antti Honkela, Richard E. Turner

* Privacy in Machine Learning Workshop (PriML 2019) at the 33rd Conference in Neural Information and Processing Systems (NeurIPS) 

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Continual Learning with Adaptive Weights (CLAW)


Nov 21, 2019
Tameem Adel, Han Zhao, Richard E. Turner


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Scalable Exact Inference in Multi-Output Gaussian Processes


Nov 14, 2019
Wessel P. Bruinsma, Eric Perim, Will Tebbutt, J. Scott Hosking, Arno Solin, Richard E. Turner

* 19 pages, 9 figures, includes appendix 

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Convolutional Conditional Neural Processes


Oct 29, 2019
Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner


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Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations


Sep 05, 2019
Jan Stühmer, Richard E. Turner, Sebastian Nowozin


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


Sep 02, 2019
Andrew Y. K. Foong, David R. Burt, Yingzhen Li, Richard E. Turner


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'In-Between' Uncertainty in Bayesian Neural Networks


Jun 27, 2019
Andrew Y. K. Foong, Yingzhen Li, José Miguel Hernández-Lobato, Richard E. Turner

* Presented at the ICML 2019 Workshop on Uncertainty and Robustness in Deep Learning 

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


Jun 18, 2019
James Requeima, Jonathan Gordon, John Bronskill, Sebastian Nowozin, Richard E. Turner


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Practical Deep Learning with Bayesian Principles


Jun 06, 2019
Kazuki Osawa, Siddharth Swaroop, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota, Mohammad Emtiyaz Khan

* Under review 

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Fast computation of loudness using a deep neural network


May 24, 2019
Josef Schlittenlacher, Richard E. Turner, Brian C. J. Moore


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Improving and Understanding Variational Continual Learning


May 06, 2019
Siddharth Swaroop, Cuong V. Nguyen, Thang D. Bui, Richard E. Turner


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


Nov 27, 2018
Thang D. Bui, Cuong V. Nguyen, Siddharth Swaroop, Richard E. Turner


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Infinite-Horizon Gaussian Processes


Nov 15, 2018
Arno Solin, James Hensman, Richard E. Turner

* To appear in Advances in Neural Information Processing Systems (NIPS 2018) 

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