David Barber

David Barber, Bert Kappen, Ali Taylan Cemgil, 2003, 2003 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (IEEE Cat. No.03TH8684).

Practical Lossless Compression with Latent Variables using Bits Back Coding pdf

David Barber, James Townsend, Tom Bird, 2019, ICLR.

Practical Gauss-Newton Optimisation for Deep Learning

David Barber, Hippolyt Ritter, Aleksandar Botev, 2017, ICML.

Bayesian Time Series Models

David Barber, Silvia Chiappa, A. Taylan Cemgil, 2011 .

Tractable Undirected Approximations for Graphical Models

David Barber, Wim Wiegerinck, W. Wiegerinck, 1998 .

Bayesian Conditional Cointegration

David Barber, Chris Bracegirdle, 2012, ICML.

Complementary Sum Sampling for Likelihood Approximation in Large Scale Classification

David Barber, Bowen Zheng, Aleksandar Botev, 2017, AISTATS.

An automatic service for the personalization of ventricular cardiac meshes

David Barber, Eric Kerfoot, Angela Lee, 2014, Journal of The Royal Society Interface.

An introduction to neural networks

David Barber, Tom Heskes, 2004 .

The junction tree algorithm

David Barber, David Barber, 2011 .

Lagrange Dual Decomposition for Finite Horizon Markov Decision Processes

David Barber, Thomas Furmston, D. Barber, 2011, ECML/PKDD.

Kernelized Infomax Clustering

David Barber, Felix V. Agakov, F. Agakov, 2005, NIPS.

Variational Cumulant Expansions for Intractable Distributions

David Barber, Piërre van de Laar, D. Barber, 2011, J. Artif. Intell. Res..

Bayesian Reasoning and Machine Learning: Learning in probabilistic models

David Barber, D. Barber, 2012 .

Bayesian reasoning and machine learning

David Barber, D. Barber, 2012 .

Variational Information Maximization for Neural Coding

David Barber, Felix V. Agakov, 2004, ICONIP.

Private Machine Learning via Randomised Response pdf

David Barber, 2020, ArXiv.

A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems

David Barber, Bertrand Mesot, D. Barber, 2006, NIPS.

Variational methods for Reinforcement Learning

David Barber, Thomas Furmston, D. Barber, 2010, AISTATS.

Dynamical Dirichlet Mixture Model

David Barber, Jean-Marc Odobez, Le Chen, 2007 .

A Bayesian Switching Linear Dynamical System for Scale-Invariant robust speech extraction

David Barber, Bertrand Mesot, 2007 .

Gaussian Mean Field Regularizes by Limiting Learned Information

David Barber, Julius Kunze, Louis Kirsch, 2019, Entropy.

EEG classification using generative independent component analysis

David Barber, Silvia Chiappa, D. Barber, 2006, Neurocomputing.

Variational Optimization pdf

David Barber, Joe Staines, D. Barber, 2012, ArXiv.

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

David Barber, Jian Li, Daxue Liu, 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Improving latent variable descriptiveness with AutoGen pdf

David Barber, Alex Mansbridge, Roberto Fierimonte, 2018, ArXiv.

Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo

David Barber, Christopher K. I. Williams, D. Barber, 1996, NIPS.

How can we provide evacuation for all building occupants

David Barber, Moira Sammut, 2009 .

Does Extra Knowledge Necessarily Improve Generalization?

David Barber, David Saad, D. Saad, 1996, Neural Computation.

Bayesian Time Series Models: Plate section

David Barber, Silvia Chiappa, A. Taylan Cemgil, 2011 .

Optimal Hebbian Learning: A Probabilistic Point of View

Jean-Pascal Pfister, David Barber, Wulfram Gerstner, 2003, ICANN.

Switching Linear Dynamical Systems for Noise Robust Speech Recognition

David Barber, Bertrand Mesot, D. Barber, 2007, IEEE Transactions on Audio, Speech, and Language Processing.

Gaussian Fields for Approximate Inference in Layered Sigmoid Belief Networks

David Barber, Peter Sollich, D. Barber, 1999, NIPS.

A Scalable Laplace Approximation for Neural Networks

David Barber, Hippolyt Ritter, Aleksandar Botev, 2018, ICLR.

Spread Divergences pdf

David Barber, Mingtian Zhang, Raza Habib, 2019, ArXiv.

generative independent component analysis for EEG classification

David Barber, Silvia Chiappa, 2005, ESANN.

Continuous-state Markov models

David Barber, D. Barber, 2011 .

Bayesian linear models

David Barber, 2011 .

S.A. Solla, T.K. Leen and K-R. M. Miller, editors, Advances in Neural Information Processing Systems

David Barber, Peter Sollich, D. Barber, 2000 .

The IM algorithm: a variational approach to Information Maximization

David Barber, Felix V. Agakov, F. Agakov, 2003, NIPS 2003.

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

David Barber, Thomas Bird, Friso H. Kingma, 2020, ArXiv.

Topic factor models: Uncovering thematic structure in equity market data

David Barber, Joe Staines, 2015, Intell. Data Anal..

Bayesian Reasoning and Machine Learning: Discrete-state Markov models

David Barber, D. Barber, 2012 .

Unified Inference for Variational Bayesian Linear Gaussian State-Space Models

David Barber, Silvia Chiappa, D. Barber, 2006, NIPS.

Bayesian Online Meta-Learning with Laplace Approximation pdf

David Barber, Hippolyt Ritter, Pau Ching Yap, 2020, ArXiv.

Using machine learning to predict hypertension from a clinical dataset

David Barber, Ken Martin, Farhana H. Zulkernine, 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).

Test Error Fluctuations in Finite Linear Perceptrons

David Barber, David Saad, Peter Sollich, 1995, Neural Computation.

Affine Independent Variational Inference

David Barber, Edward Challis, D. Barber, 2012, NIPS.

Knowledge and generalisation in simple learning systems

David Barber, David Saad, D. Saad, 1995, The European Symposium on Artificial Neural Networks.

An Auxiliary Variational Method

David Barber, Felix V. Agakov, F. Agakov, 2004, ICONIP.

Dynamic Bayesian Networks with Deterministic Latent Tables

David Barber, D. Barber, 2002, NIPS.

Stochastic POMDP controllers: How easy to optimize?

David Barber, Michael L. Littman, Nikos Vlassis, 2012 .

Radial Basis Functions: A Bayesian Treatment

David Barber, Bernhard Schottky, 1997, NIPS.

Variational f-divergence Minimization pdf

David Barber, Tianlin Xu, Mingtian Zhang, 2019, ArXiv.

Detecting patient motion on a solid state multiple pinhole dedicated cardiac camera

David Barber, Shelley Redgate, W. B. Tindale, 2012 .

Nesterov's accelerated gradient and momentum as approximations to regularised update descent

David Barber, Guy Lever, Aleksandar Botev, 2016, 2017 International Joint Conference on Neural Networks (IJCNN).

Bayesian Reasoning and Machine Learning: BRMLTOOLBOX

David Barber, D. Barber, 2012 .

Gaussian mixture models with Wasserstein distance pdf

David Barber, Ilya Feige, Benoit Gaujac, 2018, ArXiv.

On Computing the KL Divergence for Bayesian Neural Networks

David Barber, Christopher M. Bishop, C. Bishop, 1997 .

Auxiliary Variational MCMC

David Barber, Raza Habib, D. Barber, 2018, International Conference on Learning Representations.

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

Liang Xiao, David Barber, Daxue Liu, 2017, NIPS.

Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems

David Barber, D. Barber, 2006, J. Mach. Learn. Res..

Efficient Inference in Markov Control Problems

David Barber, Thomas Furmston, D. Barber, 2011, UAI.

Efficient Kalman Smoothing for Harmonic State-Space Models

David Barber, 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

Stochastic Variational Optimization pdf

David Barber, Julius Kunze, Thomas Bird, 2018, ArXiv.

Detecting Low Back Pain from Clinical Narratives Using Machine Learning Approaches

David Barber, Farhana H. Zulkernine, Michael Judd, 2018, DEXA Workshops.

Concave Gaussian Variational Approximations for Inference in Large-Scale Bayesian Linear Models

David Barber, Edward Challis, D. Barber, 2011, AISTATS.

A generative model for music transcription

David Barber, Ali Taylan Cemgil, Hilbert J. Kappen, 2006, IEEE Transactions on Audio, Speech, and Language Processing.

Bayesian Time Series Models: Preface

David Barber, Silvia Chiappa, A. Taylan Cemgil, 2011 .

Information Maximization in Noisy Channels : A Variational Approach

David Barber, Felix Agakov, F. Agakov, 2003, NIPS.

Chapter 2: Network Applications and Their Requirements

David Barber, David Barber, 2009 .

Graphical Models for Time-Series

David Barber, Ali Taylan Cemgil, D. Barber, 2010, IEEE Signal Processing Magazine.

To be published in: Advances in Neural Information Processing Systems 9,

David Barber, Peter Sollich, 2022 .

Ranking the Risks from Multiple Hazards in a Small Community

David Barber, George E. Apostolakis, Hua Li, 2009, Risk analysis : an official publication of the Society for Risk Analysis.

Learning in Spiking Neural Assemblies

David Barber, D. Barber, 2002, NIPS.

Deterministic Generative Models for Fast Feature Discovery

David Barber, Wim Wiegerinck, Machiel Westerdijk, 2004, Data Mining and Knowledge Discovery.

Learning as inference

David Barber, D. Barber, 2011 .

Optimization by Variational Bounding

David Barber, Joe Staines, D. Barber, 2013, ESANN.

Utility of linking primary care electronic medical records with Canadian census data to study the determinants of chronic disease: an example based on socioeconomic status and obesity

David Barber, Paul Belanger, Kieran Moore, 2016, BMC Medical Informatics and Decision Making.

An Image Reconstruction Algorithm for 3-D Electrical Impedance Mammography

David Barber, Wei Wang, Xiaolin Zhang, 2014, IEEE Transactions on Medical Imaging.

Stable Belief Propagation in Gaussian Dags

David Barber, Peter Sollich, 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

Variational methods for approximate reasoning in graphical models

David Barber, Tom Heskes, Hilbert J. Kappen, 2000 .

Bayesian Model Comparison by Monte Carlo Chaining

David Barber, Christopher M. Bishop, 1996, NIPS.

On-line learning from finite training sets

David Barber, Peter Sollich, 1997 .

Spread Divergence

David Barber, Mingtian Zhang, Raza Habib, 2020, ICML.

Clique Matrices for Statistical Graph Decomposition and Parameterising Restricted Positive Definite Matrices

David Barber, 2008, UAI.

Ensemble Learning for Multi-Layer Networks

David Barber, Christopher M. Bishop, Charles M. Bishop, 1997, NIPS.

Auxiliary Variational Information Maximization for Dimensionality Reduction

David Barber, Felix V. Agakov, 2005, SLSFS.

Bayesian model selection

David Barber, D. Barber, 2011 .

Letter to the Editor: Design Fires for Open-Plan Buildings with Exposed Mass-Timber Ceiling

David Barber, Panagiotis Kotsovinos, Egle Rackauskaite, 2020, Fire Technology.

Learning disentangled representations with the Wasserstein Autoencoder pdf

David Barber, Ilya Feige, Benoit Gaujac, 2020, ArXiv.

Quantification of performance requirements - good or bad for design?

David Barber, Moira Sammut, 2009 .

Online Learning from Finite Training Sets and Robustness to Input Bias

David Barber, Peter Sollich, D. Barber, 1998, Neural Computation.

Bayesian Reasoning and Machine Learning: Approximate inference

David Barber, D. Barber, 2012 .

Bayesian Reasoning and Machine Learning: Machine learning

David Barber, D. Barber, 2012 .

Probabilistic knowledge representation

David Barber, Hilbert J. Kappen, Wim Wiegerinck, 1998 .

Bayesian Classification With Gaussian Processes

David Barber, Christopher K. I. Williams, D. Barber, 1998, IEEE Trans. Pattern Anal. Mach. Intell..

Deterministic approximate inference

David Barber, D. Barber, 2011 .

Tagging of name records for genealogical data browsing

David Barber, Mike Perrow, Mike Perrow, 2006, Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL '06).

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

David Barber, Davide Piras, Alex Mansbridge, 2020, ArXiv.

Efficient inference in trees

David Barber, D. Barber, 2012 .

Bayesian Factorial Linear Gaussian State-Space Models for Biosignal Decomposition

David Barber, Silvia Chiappa, 2007, IEEE Signal Processing Letters.

Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting

David Barber, Hippolyt Ritter, Aleksandar Botev, 2018, NeurIPS.

Overdispersed variational autoencoders

David Barber, Harshil Shah, Aleksandar Botev, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

Approximate Newton Methods for Policy Search in Markov Decision Processes

David Barber, Guy Lever, Thomas Furmston, 2016, J. Mach. Learn. Res..

HiLLoC: Lossless Image Compression with Hierarchical Latent Variable Models pdf

David Barber, Julius Kunze, James Townsend, 2019, ICLR.

Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning

Jean-Pascal Pfister, David Barber, Wulfram Gerstner, 2005, Neural Computation.

On solving Ordinary Differential Equations using Gaussian Processes pdf

David Barber, 2014, ArXiv.

Modular Networks: Learning to Decompose Neural Computation

David Barber, Julius Kunze, Louis Kirsch, 2018, NeurIPS.

Generative Neural Machine Translation

David Barber, Harshil Shah, D. Barber, 2018, NeurIPS.

Nearest neighbour classification

David Barber, D. Barber, 2011 .

Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations

David Barber, Yali Wang, D. Barber, 2014, ICML.

On-line Learning from Finite Training Sets in Nonlinear Networks

David Barber, Peter Sollich, 1997, NIPS.

Improving latent variable descriptiveness by modelling rather than ad-hoc factors

David Barber, Alex Mansbridge, Roberto Fierimonte, 2019, Machine Learning.

Generating Sentences Using a Dynamic Canvas

David Barber, Bowen Zheng, Harshil Shah, 2018, AAAI.

Tractable Variational Structures for Approximating Graphical Models

David Barber, Wim Wiegerinck, W. Wiegerinck, 1998, NIPS.

A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes

David Barber, Thomas Furmston, D. Barber, 2012, NIPS.

Chapter 3: Network Checklists

David Barber, David Barber, 2009 .

Keep Taking the Tablets: Integrating the Mobile in Work-Based Learning

David Barber, 2014, BIIML.

Probabilistic Tagging of Unstructured Genealogical Records

David Barber, Mike Perrow, Mike Perrow, 2005 .

Generative vector quantisation

David Barber, Wim Wiegerinck, Machiel Westerdijk, 1999 .

On the Computational Complexity of Stochastic Controller Optimization in POMDPs

David Barber, Michael L. Littman, Nikos Vlassis, 2011, TOCT.

Finite size effects in neural network algorithms

David Barber, D. Barber, 1996 .

Machine learning concepts

David Barber, D. Barber, 2011 .

A Bayesian Alternative to Gain Adaptation in Autoregressive Hidden Markov Models

David Barber, Bertrand Mesot, 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

Sample Efficient Model Evaluation pdf

David Barber, Emine Yilmaz, Raza Habib, 2021, ArXiv.

Locally-Contextual Nonlinear CRFs for Sequence Labeling pdf

David Barber, Harshil Shah, Tim Z. Xiao, 2021, ArXiv.

Adaptive Optimization with Examplewise Gradients pdf

David Barber, Julius Kunze, James Townsend, 2021, ArXiv.

Learning disentangled representations with the Wasserstein Autoencoder

David Barber, Ilya Feige, Benoit Gaujac, 2020, ECML/PKDD.

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

David Barber, Friso H. Kingma, Thomas Bird, 2021, ICLR.

Improving Gaussian mixture latent variable model convergence with Optimal Transport

David Barber, Ilya Feige, Benoit Gaujac, 2018, ACML.

Stable Directed Belief Propagation in Gaussian DAGs using the auxiliary variable trick

David Barber, Peter Sollich, D. Barber, 2005 .

Bayesian Time Series Models: Approximate inference in switching linear dynamical systems using Gaussian mixtures

David Barber, 2011 .

Finite size effects in on-line learning of multi-layer neural networks

David Barber, David Saad, Peter Sollich, 1997 .

A Stable Switching Kalman Smoother

David Barber, 2004 .

Training generative latent models by variational f-divergence minimization

David Barber, Tianlin Xu, Raza Habib, 2018 .

Expectation Correction for an augmented class of Switching Linear Gaussian Models

David Barber, 2005 .

Finite-size effects in on-line learning of multilayer neural networks

David Barber, David Saad, Peter Sollich, 1996 .

Construction and comparison of approximations for switching linear gaussian state space models

David Barber, D. Barber, 2005 .

Affine Independent Variational Inference Supplementary Material

David Barber, Edward Challis, 2012 .

Dealing with a large number of classes -- Likelihood, Discrimination or Ranking?

David Barber, Aleksandar Botev, D. Barber, 2016, 1606.06959.

Identifying graph clusters using variational inference and links to covariance parametrization

David Barber, D. Barber, 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.