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David Barber
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2020 – today
- 2024
- [j27]Ahmed H. Shahin
, An Zhao, Alexander C. Whitehead
, Daniel C. Alexander
, Joseph Jacob
, David Barber
:
CenTime: Event-conditional modelling of censoring in survival analysis. Medical Image Anal. 91: 103016 (2024) - [c68]Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber:
Diffusive Gibbs Sampling. ICML 2024 - [c67]William Muldrew, Peter Hayes, Mingtian Zhang, David Barber:
Active Preference Learning for Large Language Models. ICML 2024 - [i48]Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber:
Diffusive Gibbs Sampling. CoRR abs/2402.03008 (2024) - [i47]William Muldrew, Peter Hayes, Mingtian Zhang, David Barber:
Active Preference Learning for Large Language Models. CoRR abs/2402.08114 (2024) - [i46]Mingtian Zhang, Shawn Lan, Peter Hayes, David Barber:
Mafin: Enhancing Black-Box Embeddings with Model Augmented Fine-Tuning. CoRR abs/2402.12177 (2024) - [i45]Rares Dolga, Marius Cobzarenco, David Barber:
Latent Attention for Linear Time Transformers. CoRR abs/2402.17512 (2024) - [i44]Zijing Ou, Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yingzhen Li, David Barber:
Diffusion Model With Optimal Covariance Matching. CoRR abs/2406.10808 (2024) - [i43]Rares Dolga, Kai Biegun, Jake Cunningham, David Barber:
RotRNN: Modelling Long Sequences with Rotations. CoRR abs/2407.07239 (2024) - 2023
- [j26]Alessia Paglialonga
, Rebecca Theal, Bruce Knox, Robert Kyba, David Barber
, Aziz Guergachi, Karim Keshavjee:
Applying Patient Segmentation Using Primary Care Electronic Medical Records to Develop a Virtual Peer-to-Peer Intervention for Patients with Type 2 Diabetes. Future Internet 15(4): 149 (2023) - [c66]Yuhao Chen, Boqun Shu, Mojtaba Moattari, Farhana H. Zulkernine, John A. Queenan, David Barber:
SPaDe: A Synonym-based Pain-level Detection Tool for Osteoarthritis. ICDH 2023: 118-120 - [c65]Mingtian Zhang, Alex Hawkins-Hooker, Brooks Paige, David Barber:
Moment Matching Denoising Gibbs Sampling. NeurIPS 2023 - [i42]David Barber:
Smoothed Q-learning. CoRR abs/2303.08631 (2023) - [i41]Yaozhi Lu, Shahab Aslani, An Zhao, Ahmed H. Shahin, David Barber, Mark Emberton, Daniel C. Alexander, Joseph Jacob:
A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study. CoRR abs/2303.10789 (2023) - [i40]Harshil Shah, Arthur Wilcke, Marius Cobzarenco, Cristi Cobzarenco, Edward Challis, David Barber:
Generalized Multiple Intent Conditioned Slot Filling. CoRR abs/2305.11023 (2023) - [i39]Mingtian Zhang, Alex Hawkins-Hooker, Brooks Paige, David Barber:
Moment Matching Denoising Gibbs Sampling. CoRR abs/2305.11650 (2023) - [i38]Ahmed H. Shahin, An Zhao, Alexander C. Whitehead, Daniel C. Alexander, Joseph Jacob, David Barber:
CenTime: Event-Conditional Modelling of Censoring in Survival Analysis. CoRR abs/2309.03851 (2023) - 2022
- [c64]An Zhao, Ahmed H. Shahin, Yukun Zhou
, Eyjolfur Gudmundsson, Adam Szmul, Nesrin Mogulkoc
, Frouke Van Beek, Christopher Brereton, Hendrik W. Van Es, Katarina Pontoppidan, Recep Savas, Timothy Wallis
, Omer Unat, Marcel Veltkamp, Mark G. Jones
, Coline H. M. Van Moorsel, David Barber, Joseph Jacob, Daniel C. Alexander
:
Prognostic Imaging Biomarker Discovery in Survival Analysis for Idiopathic Pulmonary Fibrosis. MICCAI (8) 2022: 223-233 - [c63]Ahmed H. Shahin, Joseph Jacob, Daniel C. Alexander, David Barber:
Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical Data. MIDL 2022: 1057-1074 - [c62]Alessia Paglialonga, Rebecca Theal, David Barber, Robert Kyba, Aziz Guergachi, Karim Keshavjee:
Behavioral Segmentation for Enhanced Peer-to-Peer Patient Education. MIE 2022: 125-126 - [c61]Mingtian Zhang, Peter Hayes, David Barber:
Generalization Gap in Amortized Inference. NeurIPS 2022 - [i37]Mingtian Zhang, James Townsend, Ning Kang, David Barber:
Parallel Neural Local Lossless Compression. CoRR abs/2201.05213 (2022) - [i36]Ahmed H. Shahin, Joseph Jacob, Daniel C. Alexander, David Barber:
Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical Data. CoRR abs/2203.11391 (2022) - [i35]Mingtian Zhang, Peter Hayes, David Barber:
Generalization Gap in Amortized Inference. CoRR abs/2205.11640 (2022) - [i34]Mingtian Zhang, Tim Z. Xiao, Brooks Paige, David Barber:
Improving VAE-based Representation Learning. CoRR abs/2205.14539 (2022) - [i33]Peter Hayes, Mingtian Zhang, Raza Habib, Jordan Burgess, Emine Yilmaz, David Barber:
Integrated Weak Learning. CoRR abs/2206.09496 (2022) - [i32]Mingtian Zhang, Oscar Key, Peter Hayes, David Barber, Brooks Paige, François-Xavier Briol:
Towards Healing the Blindness of Score Matching. CoRR abs/2209.07396 (2022) - 2021
- [c60]Benoit Gaujac, Ilya Feige, David Barber:
Improving Gaussian mixture latent variable model convergence with Optimal Transport. ACML 2021: 737-752 - [c59]Thomas Bird, Friso H. Kingma, David Barber:
Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks. ICLR 2021 - [c58]Pau Ching Yap, Hippolyt Ritter, David Barber:
Addressing Catastrophic Forgetting in Few-Shot Problems. ICML 2021: 11909-11919 - [c57]Benoit Gaujac, Ilya Feige, David Barber:
Learning Disentangled Representations with the Wasserstein Autoencoder. ECML/PKDD (3) 2021: 69-84 - [i31]Harshil Shah, Tim Z. Xiao, David Barber:
Locally-Contextual Nonlinear CRFs for Sequence Labeling. CoRR abs/2103.16210 (2021) - [i30]Emine Yilmaz, Peter Hayes, Raza Habib, Jordan Burgess, David Barber:
Sample Efficient Model Evaluation. CoRR abs/2109.12043 (2021) - [i29]Julius Kunze, James Townsend, David Barber:
Adaptive Optimization with Examplewise Gradients. CoRR abs/2112.00174 (2021) - 2020
- [j25]Rachael Morkem, Kenneth Handelman
, John A. Queenan, Richard Birtwhistle, David Barber:
Validation of an EMR algorithm to measure the prevalence of ADHD in the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). BMC Medical Informatics Decis. Mak. 20(1): 166 (2020) - [c56]James Townsend, Thomas Bird, Julius Kunze, David Barber:
HiLLoC: lossless image compression with hierarchical latent variable models. ICLR 2020 - [c55]Mingtian Zhang, Peter Hayes, Thomas Bird, Raza Habib, David Barber:
Spread Divergence. ICML 2020: 11106-11116 - [i28]David Barber:
Private Machine Learning via Randomised Response. CoRR abs/2001.04942 (2020) - [i27]Pau Ching Yap, Hippolyt Ritter, David Barber:
Bayesian Online Meta-Learning with Laplace Approximation. CoRR abs/2005.00146 (2020) - [i26]Benoit Gaujac, Ilya Feige, David Barber:
Learning disentangled representations with the Wasserstein Autoencoder. CoRR abs/2010.03459 (2020) - [i25]Benoit Gaujac, Ilya Feige, David Barber:
Learning Deep-Latent Hierarchies by Stacking Wasserstein Autoencoders. CoRR abs/2010.03467 (2020) - [i24]Alex Mansbridge, Gregory Barbour, Davide Piras
, Christopher Frye, Ilya Feige, David Barber:
Learning to Noise: Application-Agnostic Data Sharing with Local Differential Privacy. CoRR abs/2010.12464 (2020) - [i23]Thomas Bird, Friso H. Kingma, David Barber:
Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks. CoRR abs/2010.13476 (2020)
2010 – 2019
- 2019
- [j24]Julius Kunze, Louis Kirsch, Hippolyt Ritter, David Barber
:
Gaussian Mean Field Regularizes by Limiting Learned Information. Entropy 21(8): 758 (2019) - [j23]Alex Mansbridge
, Roberto Fierimonte, Ilya Feige, David Barber:
Improving latent variable descriptiveness by modelling rather than ad-hoc factors. Mach. Learn. 108(8-9): 1601-1611 (2019) - [c54]Zhen He, Jian Li
, Daxue Liu, Hangen He, David Barber:
Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers. CVPR 2019: 1318-1327 - [c53]Raza Habib, David Barber:
Auxiliary Variational MCMC. ICLR (Poster) 2019 - [c52]James Townsend, Thomas Bird, David Barber:
Practical lossless compression with latent variables using bits back coding. ICLR (Poster) 2019 - [i22]James Townsend, Tom Bird, David Barber:
Practical Lossless Compression with Latent Variables using Bits Back Coding. CoRR abs/1901.04866 (2019) - [i21]Julius Kunze, Louis Kirsch, Hippolyt Ritter, David Barber:
Gaussian Mean Field Regularizes by Limiting Learned Information. CoRR abs/1902.04340 (2019) - [i20]Mingtian Zhang, Thomas Bird, Raza Habib, Tianlin Xu, David Barber:
Variational f-divergence Minimization. CoRR abs/1907.11891 (2019) - [i19]James Townsend, Thomas Bird, Julius Kunze, David Barber:
HiLLoC: Lossless Image Compression with Hierarchical Latent Variable Models. CoRR abs/1912.09953 (2019) - 2018
- [c51]Harshil Shah, Bowen Zheng, David Barber:
Generating Sentences Using a Dynamic Canvas. AAAI 2018: 5430-5437 - [c50]Michael Judd, Farhana H. Zulkernine, Brent Wolfrom, David Barber, Akshay Rajaram:
Detecting Low Back Pain from Clinical Narratives Using Machine Learning Approaches. DEXA Workshops 2018: 126-137 - [c49]Hippolyt Ritter, Aleksandar Botev, David Barber:
A Scalable Laplace Approximation for Neural Networks. ICLR (Poster) 2018 - [c48]Harshil Shah, David Barber:
Generative Neural Machine Translation. NeurIPS 2018: 1353-1362 - [c47]Louis Kirsch, Julius Kunze, David Barber:
Modular Networks: Learning to Decompose Neural Computation. NeurIPS 2018: 2414-2423 - [c46]Hippolyt Ritter, Aleksandar Botev, David Barber:
Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting. NeurIPS 2018: 3742-3752 - [i18]Hippolyt Ritter, Aleksandar Botev, David Barber:
Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting. CoRR abs/1805.07810 (2018) - [i17]Benoit Gaujac, Ilya Feige, David Barber:
Gaussian mixture models with Wasserstein distance. CoRR abs/1806.04465 (2018) - [i16]Alex Mansbridge, Roberto Fierimonte, Ilya Feige, David Barber:
Improving latent variable descriptiveness with AutoGen. CoRR abs/1806.04480 (2018) - [i15]Harshil Shah, David Barber:
Generative Neural Machine Translation. CoRR abs/1806.05138 (2018) - [i14]Harshil Shah, Bowen Zheng, David Barber:
Generating Sentences Using a Dynamic Canvas. CoRR abs/1806.05178 (2018) - [i13]Zhen He, Jian Li, Daxue Liu, Hangen He, David Barber:
Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers. CoRR abs/1809.03137 (2018) - [i12]Thomas Bird, Julius Kunze, David Barber:
Stochastic Variational Optimization. CoRR abs/1809.04855 (2018) - [i11]Louis Kirsch, Julius Kunze, David Barber:
Modular Networks: Learning to Decompose Neural Computation. CoRR abs/1811.05249 (2018) - [i10]David Barber, Mingtian Zhang, Raza Habib, Thomas Bird:
Spread Divergences. CoRR abs/1811.08968 (2018) - 2017
- [c45]Aleksandar Botev, Bowen Zheng, David Barber:
Complementary Sum Sampling for Likelihood Approximation in Large Scale Classification. AISTATS 2017: 1030-1038 - [c44]Aleksandar Botev, Hippolyt Ritter, David Barber:
Practical Gauss-Newton Optimisation for Deep Learning. ICML 2017: 557-565 - [c43]Harshil Shah, David Barber, Aleksandar Botev
:
Overdispersed variational autoencoders. IJCNN 2017: 1109-1116 - [c42]Aleksandar Botev
, Guy Lever, David Barber:
Nesterov's accelerated gradient and momentum as approximations to regularised update descent. IJCNN 2017: 1899-1903 - [c41]Zhen He, Shaobing Gao, Liang Xiao, Daxue Liu, Hangen He, David Barber:
Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning. NIPS 2017: 1-11 - [c40]Thomas Anthony, Zheng Tian, David Barber:
Thinking Fast and Slow with Deep Learning and Tree Search. NIPS 2017: 5360-5370 - [i9]Thomas Anthony, Zheng Tian, David Barber:
Thinking Fast and Slow with Deep Learning and Tree Search. CoRR abs/1705.08439 (2017) - [i8]Zhen He, Shaobing Gao, Liang Xiao, Daxue Liu, Hangen He, David Barber:
Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning. CoRR abs/1711.01577 (2017) - 2016
- [j22]Thomas Furmston, Guy Lever, David Barber:
Approximate Newton Methods for Policy Search in Markov Decision Processes. J. Mach. Learn. Res. 17: 227:1-227:51 (2016) - [j21]Suzanne Biro, Tyler S. Williamson
, Jannet Ann Leggett, David Barber, Rachael Morkem, Kieran Moore, Paul Belanger, Brian Mosley, Ian Janssen
:
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. BMC Medical Informatics Decis. Mak. 16: 32 (2016) - [c39]Daniel Lafrenière, Farhana H. Zulkernine, David Barber, Ken Martin:
Using machine learning to predict hypertension from a clinical dataset. SSCI 2016: 1-7 - [i7]Aleksandar Botev, Guy Lever, David Barber:
Nesterov's Accelerated Gradient and Momentum as approximations to Regularised Update Descent. CoRR abs/1607.01981 (2016) - 2015
- [j20]Joe Staines, David Barber:
Topic factor models: Uncovering thematic structure in equity market data. Intell. Data Anal. 19(s1): S69-S85 (2015) - 2014
- [j19]Xiaolin Zhang, Wei Wang, Gerald Sze, David Barber, Chris R. Chatwin:
An Image Reconstruction Algorithm for 3-D Electrical Impedance Mammography. IEEE Trans. Medical Imaging 33(12): 2223-2241 (2014) - [c38]David Barber:
Keep Taking the Tablets: Integrating the Mobile in Work-Based Learning. BIIML 2014 - [c37]David Barber, Yali Wang:
Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations. ICML 2014: 1485-1493 - [i6]David Barber:
On solving Ordinary Differential Equations using Gaussian Processes. CoRR abs/1408.3807 (2014) - 2013
- [j18]Edward Challis, David Barber:
Gaussian Kullback-Leibler approximate inference. J. Mach. Learn. Res. 14(1): 2239-2286 (2013) - [c36]Joe Staines, David Barber:
Optimization by Variational Bounding. ESANN 2013 - 2012
- [b2]David Barber:
Bayesian reasoning and machine learning. Cambridge University Press 2012, ISBN 0521518148, pp. I-XXIV, 1-697 - [j17]Nikos Vlassis, Michael L. Littman, David Barber:
On the Computational Complexity of Stochastic Controller Optimization in POMDPs. ACM Trans. Comput. Theory 4(4): 12:1-12:8 (2012) - [c35]Chris Bracegirdle, David Barber:
Bayesian Conditional Cointegration. ICML 2012 - [c34]Edward Challis, David Barber:
Affine Independent Variational Inference. NIPS 2012: 2195-2203 - [c33]Thomas Furmston, David Barber:
A Unifying Perspective of Parametric Policy Search Methods for Markov Decision Processes. NIPS 2012: 2726-2734 - [i5]Thomas Furmston, David Barber:
Efficient Inference in Markov Control Problems. CoRR abs/1202.3720 (2012) - [i4]David Barber:
Clique Matrices for Statistical Graph Decomposition and Parameterising Restricted Positive Definite Matrices. CoRR abs/1206.3237 (2012) - [i3]Joe Staines, David Barber:
Variational Optimization. CoRR abs/1212.4507 (2012) - 2011
- [c32]Thomas Furmston, David Barber:
Lagrange Dual Decomposition for Finite Horizon Markov Decision Processes. ECML/PKDD (1) 2011: 487-502 - [c31]Thomas Furmston, David Barber:
Efficient Inference in Markov Control Problems. UAI 2011: 221-229 - [c30]Chris Bracegirdle, David Barber:
Switch-Reset Models : Exact and Approximate Inference. AISTATS 2011: 190-198 - [c29]Edward Challis, David Barber:
Concave Gaussian Variational Approximations for Inference in Large-Scale Bayesian Linear Models. AISTATS 2011: 199-207 - [i2]David Barber, Piërre van de Laar:
Variational Cumulant Expansions for Intractable Distributions. CoRR abs/1105.5455 (2011) - [i1]Nikos Vlassis, Michael L. Littman, David Barber:
On the computational complexity of stochastic controller optimization in POMDPs. CoRR abs/1107.3090 (2011) - 2010
- [j16]David Barber, A. Taylan Cemgil
:
Graphical Models for Time-Series. IEEE Signal Process. Mag. 27(6): 18-28 (2010) - [c28]Thomas Furmston, David Barber:
Variational methods for Reinforcement Learning. AISTATS 2010: 241-248
2000 – 2009
- 2009
- [j15]Bertrand Mesot, David Barber:
A Simple Alternative Derivation of the Expectation Correction Algorithm. IEEE Signal Process. Lett. 16(2): 121-124 (2009) - 2008
- [c27]David Barber:
Clique Matrices for Statistical Graph Decomposition and Parameterising Restricted Positive Definite Matrices. UAI 2008: 26-33 - 2007
- [j14]Silvia Chiappa, David Barber:
Bayesian Factorial Linear Gaussian State-Space Models for Biosignal Decomposition. IEEE Signal Process. Lett. 14(4): 267-270 (2007) - [j13]Bertrand Mesot, David Barber:
Switching Linear Dynamical Systems for Noise Robust Speech Recognition. IEEE Trans. Speech Audio Process. 15(6): 1850-1858 (2007) - [c26]David Barber, Peter Sollich:
Stable Belief Propagation in Gaussian Dags. ICASSP (2) 2007: 409-412 - [c25]Bertrand Mesot, David Barber:
A Bayesian Alternative to Gain Adaptation in Autoregressive Hidden Markov Models. ICASSP (2) 2007: 437-440 - 2006
- [j12]Silvia Chiappa, David Barber:
EEG classification using generative independent component analysis. Neurocomputing 69(7-9): 769-777 (2006) - [j11]David Barber:
Expectation Correction for Smoothed Inference in Switching Linear Dynamical Systems. J. Mach. Learn. Res. 7: 2515-2540 (2006) - [j10]Jean-Pascal Pfister
, Taro Toyoizumi
, David Barber, Wulfram Gerstner
:
Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning. Neural Comput. 18(6): 1318-1348 (2006) - [j9]Ali Taylan Cemgil
, Hilbert J. Kappen, David Barber:
A generative model for music transcription. IEEE Trans. Speech Audio Process. 14(2): 679-694 (2006) - [c24]David Barber:
Efficient Kalman Smoothing for Harmonic State-Space Models. ICASSP (3) 2006: 528-531 - [c23]Mike Perrow, David Barber:
Tagging of name records for genealogical data browsing. JCDL 2006: 316-325 - [c22]David Barber, Silvia Chiappa:
Unified Inference for Variational Bayesian Linear Gaussian State-Space Models. NIPS 2006: 81-88 - [c21]David Barber, Bertrand Mesot:
A Novel Gaussian Sum Smoother for Approximate Inference in Switching Linear Dynamical Systems. NIPS 2006: 89-96 - 2005
- [j8]David Barber:
Islands of the Arctic. Cartogr. Int. J. Geogr. Inf. Geovisualization 40(3): 128-129 (2005) - [c20]Silvia Chiappa, David Barber:
generative independent component analysis for EEG classification. ESANN 2005: 297-302 - [c19]Jean-François Paiement, Douglas Eck, Samy Bengio, David Barber:
A graphical model for chord progressions embedded in a psychoacoustic space. ICML 2005: 641-648 - [c18]Felix V. Agakov, David Barber:
Kernelized Infomax Clustering. NIPS 2005: 17-24 - [c17]Felix V. Agakov, David Barber:
Auxiliary Variational Information Maximization for Dimensionality Reduction. SLSFS 2005: 103-114 - 2004
- [c16]Felix V. Agakov, David Barber:
Variational Information Maximization for Neural Coding. ICONIP 2004: 543-548 - [c15]Felix V. Agakov, David Barber:
An Auxiliary Variational Method. ICONIP 2004: 561-566 - 2003
- [c14]Jean-Pascal Pfister
, David Barber, Wulfram Gerstner:
Optimal Hebbian Learning: A Probabilistic Point of View. ICANN 2003: 92-98 - [c13]Felix V. Agakov, David Barber:
Approximate Learning in Temporal Hidden Hopfield Models. ICANN 2003: 107-114 - [c12]David Barber, Felix V. Agakov:
Information Maximization in Noisy Channels : A Variational Approach. NIPS 2003: 201-208 - 2002
- [c11]David Barber:
Learning in Spiking Neural Assemblies. NIPS 2002: 149-156 - [c10]David Barber:
Dynamic Bayesian Networks with Deterministic Latent Tables. NIPS 2002: 713-720 - 2001
- [j7]Machiel Westerdijk, David Barber, Wim Wiegerinck:
Deterministic Generative Models for Fast Feature Discovery. Data Min. Knowl. Discov. 5(4): 337-363 (2001)
1990 – 1999
- 1999
- [j6]David Barber, Piërre van de Laar
:
Variational Cumulant Expansions for Intractable Distributions. J. Artif. Intell. Res. 10: 435-455 (1999) - [c9]David Barber, Peter Sollich:
Gaussian Fields for Approximate Inference in Layered Sigmoid Belief Networks. NIPS 1999: 393-399 - 1998
- [j5]Peter Sollich
, David Barber:
Online Learning from Finite Training Sets and Robustness to Input Bias. Neural Comput. 10(8): 2201-2217 (1998) - [j4]Christopher K. I. Williams, David Barber:
Bayesian Classification With Gaussian Processes. IEEE Trans. Pattern Anal. Mach. Intell. 20(12): 1342-1351 (1998) - [c8]David Barber, Wim Wiegerinck:
Tractable Variational Structures for Approximating Graphical Models. NIPS 1998: 183-189 - 1997
- [j3]David Barber:
OhioLINK: A Consortial Approach to Digital Library Management. D Lib Mag. 3(4) (1997) - [c7]Peter Sollich, David Barber:
On-line Learning from Finite Training Sets in Nonlinear Networks. NIPS 1997: 357-363 - [c6]David Barber, Christopher M. Bishop:
Ensemble Learning for Multi-Layer Networks. NIPS 1997: 395-401 - [c5]David Barber, Bernhard Schottky:
Radial Basis Functions: A Bayesian Treatment. NIPS 1997: 402-408 - 1996
- [b1]David Barber:
Finite size effects in neural network algorithms. University of Edinburgh, UK, 1996 - [j2]David Barber, David Saad
:
Does Extra Knowledge Necessarily Improve Generalization? Neural Comput. 8(1): 202-214 (1996) - [c4]Peter Sollich, David Barber:
Online Learning from Finite Training Sets: An Analytical Case Study. NIPS 1996: 274-280 - [c3]David Barber, Christopher M. Bishop:
Bayesian Model Comparison by Monte Carlo Chaining. NIPS 1996: 333-339 - [c2]David Barber, Christopher K. I. Williams:
Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo. NIPS 1996: 340-346 - 1995
- [j1]David Barber, David Saad, Peter Sollich
:
Test Error Fluctuations in Finite Linear Perceptrons. Neural Comput. 7(4): 809-821 (1995) - [c1]David Barber, David Saad:
Knowledge and generalisation in simple learning systems. ESANN 1995
Coauthor Index
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