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Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical Data


Mar 21, 2022
Ahmed H. Shahin, Joseph Jacob, Daniel C. Alexander, David Barber

* Accepted as a full paper at the Medical Imaging with Deep Learning conference (MIDL 2022) 

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Parallel Neural Local Lossless Compression


Jan 23, 2022
Mingtian Zhang, James Townsend, Ning Kang, David Barber


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Adaptive Optimization with Examplewise Gradients


Nov 30, 2021
Julius Kunze, James Townsend, David Barber

* 9 pages, 1 figure, 3 tables 

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Sample Efficient Model Evaluation


Sep 24, 2021
Emine Yilmaz, Peter Hayes, Raza Habib, Jordan Burgess, David Barber


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Locally-Contextual Nonlinear CRFs for Sequence Labeling


Mar 30, 2021
Harshil Shah, Tim Xiao, David Barber


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Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks


Oct 26, 2020
Thomas Bird, Friso H. Kingma, David Barber


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Learning to Noise: Application-Agnostic Data Sharing with Local Differential Privacy


Oct 23, 2020
Alex Mansbridge, Gregory Barbour, Davide Piras, Christopher Frye, Ilya Feige, David Barber


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Learning Deep-Latent Hierarchies by Stacking Wasserstein Autoencoders


Oct 07, 2020
Benoit Gaujac, Ilya Feige, David Barber


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Learning disentangled representations with the Wasserstein Autoencoder


Oct 07, 2020
Benoit Gaujac, Ilya Feige, David Barber


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Bayesian Online Meta-Learning with Laplace Approximation


Apr 30, 2020
Pau Ching Yap, Hippolyt Ritter, David Barber


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Private Machine Learning via Randomised Response


Feb 24, 2020
David Barber


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HiLLoC: Lossless Image Compression with Hierarchical Latent Variable Models


Dec 20, 2019
James Townsend, Thomas Bird, Julius Kunze, David Barber


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Variational f-divergence Minimization


Jul 27, 2019
Mingtian Zhang, Thomas Bird, Raza Habib, Tianlin Xu, David Barber


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Gaussian Mean Field Regularizes by Limiting Learned Information


Feb 12, 2019
Julius Kunze, Louis Kirsch, Hippolyt Ritter, David Barber


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Practical Lossless Compression with Latent Variables using Bits Back Coding


Jan 15, 2019
James Townsend, Tom Bird, David Barber


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Spread Divergences


Dec 02, 2018
David Barber, Mingtian Zhang, Raza Habib, Thomas Bird


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Modular Networks: Learning to Decompose Neural Computation


Nov 13, 2018
Louis Kirsch, Julius Kunze, David Barber

* NIPS 2018 

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Stochastic Variational Optimization


Sep 13, 2018
Thomas Bird, Julius Kunze, David Barber


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Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers


Sep 10, 2018
Zhen He, Jian Li, Daxue Liu, Hangen He, David Barber

* Submitted to AAAI 2019 

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Generative Neural Machine Translation


Jun 13, 2018
Harshil Shah, David Barber


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Generating Sentences Using a Dynamic Canvas


Jun 13, 2018
Harshil Shah, Bowen Zheng, David Barber

* AAAI 2018 

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Improving latent variable descriptiveness with AutoGen


Jun 12, 2018
Alex Mansbridge, Roberto Fierimonte, Ilya Feige, David Barber

* 8 pages, 2 figures, 5 tables 

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Gaussian mixture models with Wasserstein distance


Jun 12, 2018
Benoit Gaujac, Ilya Feige, David Barber

* 8 pages, 5 figures 

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Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting


May 20, 2018
Hippolyt Ritter, Aleksandar Botev, David Barber

* 13 pages, 6 figures 

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Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning


Dec 13, 2017
Zhen He, Shaobing Gao, Liang Xiao, Daxue Liu, Hangen He, David Barber

* Accepted by NIPS 2017 

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Thinking Fast and Slow with Deep Learning and Tree Search


Dec 03, 2017
Thomas Anthony, Zheng Tian, David Barber

* v1 to v2: - Add a value function in MCTS - Some MCTS hyper-parameters changed - Repetition of experiments: improved accuracy and errors shown. (note the reduction in effect size for the tpt/cat experiment) - Results from a longer training run, including changes in expert strength in training - Comparison to MoHex. v3: clarify independence of ExIt and AG0. v4: see appendix E 

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Practical Gauss-Newton Optimisation for Deep Learning


Jun 13, 2017
Aleksandar Botev, Hippolyt Ritter, David Barber

* ICML 2017 

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Nesterov's Accelerated Gradient and Momentum as approximations to Regularised Update Descent


Jul 11, 2016
Aleksandar Botev, Guy Lever, David Barber


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