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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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