Online papers: Chris Williams

Chris Williams: Papers available online

2012

A Generative Model for Parts-based Object Segmentation pdf, supplementary material: S. M. Ali Eslami, Christopher K. I. Williams
In Advances in Neural Information Processing Systems 25, 2012, eds P. Bartlett, F.C.N. Pereira, C.J.C. Burges, L. Bottou and K.Q. Weinberger.
A Framework for Evaluating Approximation Methods for Gaussian Process Regression: Krzysztof Chalupka, Christopher K. I. Williams, Iain Murray
v2 posted on arXiv, 5 Nov 2012.
Accepted for publication in Journal of Machine Learning Research 17 Dec 2012, published in vol 14 pp 333-350 (2013) JMLR pdf. link to code and data.
Multiple Texture Boltzmann Machines pdf, supplementary material: J. J. Kivinen and Christopher K. I. Williams
In Proceedings AISTATS 2012, Volume 22 of JMLR W&CP.

2011

Factored Shapes and Appearances for Parts-based Object Understanding pdf, supplementary material, abstract: S. M. Ali Eslami and Christopher K. I. Williams
In Proceedings BMVC 2011
Transformation Equivariant Boltzmann Machines pdf: Jyri J. Kivinen and Christopher K. I. Williams
In Proceedings ICANN 2011, eds. T. Honkela et al, LNCS 6791, Springer-Verlag (2011).
Automating the Calibration of a Neonatal Condition Monitoring System pdf: Christopher K. I. Williams and Ioan Stansculescu
In Proc AIME 2011, eds M. Peleg, N. Lavrač, and C. Combi, LNAI 6747, pp. 240--249. Springer (2011)
Milepost GCC: Machine Learning Enabled Self-tuning Compiler pdf: Grigori Fursin, Yuriy Kashnikov, Abdul Wahid Memon, Zbigniew Chamski, Olivier Temam, Mircea Namolaru, Elad Yom-Tov, Bilha Mendelson, Ayal Zaks, Eric Courtois, Francois Bodin, Phil Barnard, Elton Ashton, Edwin Bonilla, John Thomson, Christopher K. I. Williams, Michael O'Boyle.
International Journal of Parallel Programming 39(3) pp 296-327 (2011)
DOI:10.1007/s10766-010-0161-2

2010

Advances in Neural Information Processing Systems 23: editors J. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R. S. Zemel, and A. Culotta, (2010).
Kick-starting GPLVM Optimization via a Connection to Metric MDS pdf: S. Bitzer and C. K. I.Williams
In Proceedings of the NIPS 2010 workshop on Challenges of Data Visualization, December 2010.
Physiological Monitoring with Factorial Switching Linear Dynamical Systems pdf: J.A. Quinn and C.K.I. Williams.
Chapter appearing in Bayesian Time Series Models, eds. D. Barber, A. T. Cemgil, S. Chiappa, Cambridge University Press, 2011.
Greedy Learning of Binary Latent Trees pdf: S. Harmeling and C. K. I. Williams
Final manuscript version of the paper accepted to appear in IEEE PAMI.
Published in IEEE Transactions on Pattern Analysis and Machine Intelligence 33(6) 1087-1097 (2011).
Matlab code is available.
The PASCAL Visual Object Classes (VOC) Challenge pdf: M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, A. Zisserman
Final manuscript version of the paper in International Journal of Computer Vision 88(2), 303-338 (2010)

2009

Advances in Neural Information Processing Systems 22: editors Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams and A. Culotta, (2009).
Learning generative texture models with extended Fields-of-Experts pdf, supplementary material: N. Heess, C. K. I. Williams, G. E. Hinton
Proceedings BMVC 2009
Object localisation using the Generative Template of Features pdf: M. Allan, C. K. I. Williams
Final manuscript version of the paper in Computer Vision and Image Understanding 113 824-838 (2009)
Multi-task Gaussian Process Learning of Robot Inverse Dynamics pdf: K. M. A. Chai, C. K. I. Williams, S. Klanke, S. Vijayakumar
In Advances in Neural Information Processing Systems 21, eds D. Koller, Y. Bengio, D. Schuurmans, L. Bottou (2009)
A new method of spike modelling and interval analysis: D.J. MacGregor, C.K.I. Williams, G. Leng.
Journal of Neuroscience Methods, 176(1) 2009, Pages 45-56, doi:10.1016/j.jneumeth.2008.08.011

2008

Factorial Switching Linear Dynamical Systems applied to Physiological Condition Monitoring pdf: John A. Quinn, Christopher K.I. Williams, Neil McIntosh.
Accepted to IEEE Trans. on Pattern Analysis and Machine Intelligence (July 2008), published T-PAMI 31(9) pp 1537-1551 (2009). Matlab code is available.
MILEPOST GCC: machine learning based research compiler pdf: G. Fursin, C. Miranda. O. Teman et al [including C. K. I. Williams], Proceedings of the GCC Developers' Summit, 2008
Signal Masking in Gaussian Channels pdf: John A. Quinn, Christopher K. I. Williams.
Proc ICASSP 2008.
Multi-task Gaussian Process Prediction pdf: Edwin V. Bonilla, Kian Ming A. Chai, Christopher K. I. Williams.
In Advances in Neural Information Processing Systems 20, eds. J. C. Platt, D. Koller, Y. Singer, S. Roweis, MIT Press (2008)
NB See this correction note concerning the results reported in section 6 (Jan 2009)

2007

EDI-INF-RR-1228 A Note on Noise-free Gaussian Process Prediction with Separable Covariance Functions and Grid Designs pdf: Christopher K. I. Williams, Kian Ming A. Chai, Edwin V. Bonilla
Informatics Research Report, December 2007.
Approximation Methods for Gaussian Process Regression pdf: Joaquin Quinonero-Candela, Carl Edward Rasmussen, Christopher K. I. Williams.
Final draft of a chapter in Large Scale Kernel Machines eds. L. Bottou, O. Chapelle, D. DeCoste, J. Weston, pages 203-223 MIT Press, 2007
Known Unknowns: Novelty Detection in Condition Monitoring pdf: John A. Quinn, Christopher K. I. Williams.
Invited paper in Proc 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007),
eds J. Marti, J. M. Benedi, A. M. Mendonca, J. Serrat, LNCS 4477 pp 1-6, Springer-Verlag
Kernel Multi-task Learning using Task-specific Features pdf: Edwin V. Bonilla, Felix V. Agakov, Christopher K. I. Williams. Proc AISTATS 2007.

2006

On the Extension of Eigenvectors to New Datapoints pdf: C. K. I. Williams. Technical note, November 2006.
Sequential Learning of Layered Models from Video pdf: M. K. Titsias, C. K. I. Williams. In Toward Category-Level Object Recognition, eds. J. Ponce, M. Hebert, C. Schmid, and A. Zisserman, LNCS 4170, Springer-Verlag, pp 577-595, 2006.
Dataset Issues in Object Recognition pdf: J. Ponce, T.L. Berg, M.R. Everingham, D.A. Forsyth, M. Hebert, S. Lazebnik, M. Marszalek, C. Schmid, B.C. Russell, A. Torralba, C.K.I. Williams, J. Zhang, and A. Zisserman. In Toward Category-Level Object Recognition, eds. J. Ponce, M. Hebert, C. Schmid, and A. Zisserman, LNCS 4170, Springer-Verlag, pp 29-48 2006.
The PASCAL Visual Object Classes Challenge 2006 (VOC 2006) Results pdf: Mark Everingham, Andrew Zisserman, Chris Williams, Luc Van Gool
Technical Report, September 2006.
Predictive Search Distributions pdf: Edwin V. Bonilla, Christopher K. I. Williams, Felix V. Agakov, John Cavazos, John Thomson, Michael F.P. O'Boyle
In Proc. ICML 2006
Using Machine Learning to Focus Iterative Optimization pdf: F. Agakov, E. Bonilla, J. Cavazos, B. Franke, G. Fusin, M. F. P. O'Boyle, J. Thomson, M. Toussaint, C. K. I. Williams.
The 4th Annual International Symposium on Code Generation and Optimization (CGO), March 2006.
A regularized discriminative model for the prediction of protein-peptide interactions: Wolfgang P. Lehrach, Dirk Husmeier, Christopher K. I. Williams
Bioinformatics 22(5):532-540 (2006)
EDI-INF-RR-0719 On a Connection between Object Localization with a Generative Template of Features and Pose-space Prediction Methods pdf: Christopher K. I. Williams and Moray Allan
Informatics Research Report, January 2006.
Gaussian Processes for Machine Learning: Carl Edward Rasmussen and Christopher K. I. Williams, MIT Press (2006).
Book website, MIT Press site
Factorial Switching Kalman Filters for Condition Monitoring in Neonatal Intensive Care pdf: Christopher K. I. Williams, John Quinn, Neil McIntosh
To appear in Advances in Neural Information Processing Systems 18, eds. Y. Weiss, B. Schoelkopf, J. C. Platt, MIT Press (2006)

2005

Unsupervised Learning of Multiple Aspects of Moving Objects from Video pdf: Michalis K. Titsias, Christopher K. I. Williams
In: Advances in Informatics, 10th Panhellenic Conference on Informatics, PCI 2005, Volos, Greece, November 11-13, 2005, LNCS 3746 Springer (2005), pp 746-756, © Springer-Verlag.
The 2005 PASCAL Visual Object Classes Challenge pdf: M. Everingham, A. Zisserman, C. K. I. Williams, L. Van Gool, et al.
In Machine Learning Challenges, eds. J. Quinonero-Candela, I. Dagan, B. Magnini, F. d'Alche-Buc, LNAI 3944, Springer-Verlag, 2006.
EDI-INF-RR-0318 An Expectation Maximisation Algorithm for One-to-Many Record Linkage, Illustrated on the Problem of Matching Far Infra-Red Astronomical Sources to Optical Counterparts: A. J. Storkey, C. K. I. Williams, E. Taylor, R. G. Mann
Informatics Research Report, August 2005.
Understanding Gaussian Process Regression Using the Equivalent Kernel pdf: Peter Sollich, Christopher K. I. Williams
In: Deterministic and Statistical Methods in Machine Learning, eds. J. Winkler, N. D. Lawrence and M. Niranjan, LNAI 3635, © Springer-Verlag.
Fast Learning of Sprites using Invariant Fetaures pdf: Moray Allan, Michalis K. Titsias, Christopher K. I. Williams
In Proc. British Machine Vision Conference 2005 (BMVC 2005)
Video sequences.
The Impact of Using Related Individuals for Haplotype Reconstruction in Population Studies pdf: Michael T. Schouten, Christopher K. I. Williams, Chris S. Haley
Genetics 171(3) 1321-1330 (2005)
On the Eigenspectrum of the Gram Matrix and the Generalization Error of Kernel PCA pdf: John Shawe-Taylor, Christopher K. I. Williams, Nello Cristianini, Jaz Kandola
IEEE Tranactions on Information Theory 51(7) 2510-2522 (2005)
Using the Equivalent Kernel to Understand Gaussian Process Regression pdf: Peter Sollich, Christopher K. I. Williams
Advances in Neural Information Processing Systems 17 MIT Press (2005)
Harmonising Chorales by Probabilistic Inference pdf: Moray Allan, Christopher K. I. Williams
Advances in Neural Information Processing Systems 17 MIT Press (2005)
How to pretend that correlated variables are independent by using difference observations pdf: Christopher K. I. Williams
Neural Computation 17(1) 1-6 (2005)

2004

Fast Unsupervised Greedy Learning of Multiple Objects and Parts from Video pdf: Michalis K. Titsias, Christopher K. I. Williams
Persented at Generative-Model Based Vision Workshop held in conjunction with CVPR, 2004, published in 2004 CVPR Workshop vol 12
Greedy Learning of Multiple Objects in Images using Robust Statistics and Factorial Learning pdf: Christopher K. I. Williams, Michalis K. Titsias
Neural Computation 16(5) 1039-1062 (2004).
Extreme Components Analysis pdf: Max Welling, Felix V. Agakov, Christopher K. I. Williams
Advances in Neural Information Processing Systems 16 eds. S. Thrun, L. Saul, B. Schoelkopf, MIT Press (2004).

2003

EDI-INF-RR-0185 An isotropic Gaussian mixture can have more modes than components: Miguel A. Carreira-Perpinan, Christopher K. I. Williams
Informatics Research Report, December 2003.

See Miguel's webpage for some animations relating to these results.
Cleaning Sky Survey Databases using Hough Transform and Renewal String Approaches electronic versions available: Storkey A.J., N.C. Hambly, C.K.I. Williams, R.G. Mann
Monthly Notices of the Royal Astronomical Society 347, 36-51(2003).
Renewal Strings for Cleaning Astronomical Databases electronic versions available: Storkey A.J., N.C. Hambly, C.K.I. Williams, R.G. Mann
In Uncertainty in Artificial Intelligence: Proceedings of the Nineteenth Conference (UAI-2003), 559-566.
Dynamic Trees for Image Modelling pdf: Nicholas J. Adams, Christopher K. I. Williams
Final draft of paper appearing in Image and Vision Computing 20(10) 865-877 (2003)
EDI-INF-RR-0159 On the Number of Modes of a Gaussian Mixture: Miguel A. Carreira-Perpinan, Christopher K. I. Williams
Informatics Research Report, February 2003.
A slightly shortened version appears in Scale Space '03 (Proceedings of the 4th International Conference on Scale Space Theories in Computer Vision, June 2003).
Note (Dec 03): The conjecture given in this paper that if the components of the mixture have the same covariance matrix then the number of modes cannot exceed the number of components is false. See EDI-INF-RR-0185 for details.
Learning About Multiple Objects in Images: Factorial Learning without Factorial Search gzipped postscript: Christopher K. I. Williams, Michalis K. Titsias
Advances in Neural Information Processing Systems 15 eds. S. Becker, S. Thrun, K. Obermayer MIT Press (2003)
The Stability of Kernel Principal Components Analysis and its Relation to the Process Eigenspectrum gzipped postscript: John Shawe-Taylor, Christopher K. I. Williams
Advances in Neural Information Processing Systems 15 eds. S. Becker, S. Thrun, K. Obermayer MIT Press (2003)
A longer version of this paper (with author list John Shawe-Taylor, Chris Williams, Nello Cristianini, and Jaz Kandola) also appeared in the Proceedings of the 5th International Conference on Discovery Science, eds Steffen Lange, Ken Satoh, and Carl H. Smith, Lecture Notes in Computer Science vol 2534, Springer-Verlag (2002).

2002

UKeS-2002-06 Scientific Data Mining, Integration and Visualisation: Bob Mann, Roy Williams, Malcolm Atkinson, Ken Brodlie, Amos Storkey, Chris Williams.
Final Report of the meeting on Scientific Data Mining, Integration and Visualisation held at the eScience Institute, Edinburgh, 24-25 October 2002.
Image modelling with position-encoding dynamic trees pdf: Amos J. Storkey, Christopher K. I. Williams.
Final draft of paper appearing in IEEE Pattern Analysis and Machine Intelligence 25(7) 859-871 (2003).
IEEE legal stuff This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without explicit permission of the copyright holder.
Fast Forward Selection to Speed Up Sparse Gaussian Process Regression gzipped postscript: Matthias Seeger, Christopher K.I. Williams and Neil Lawrence.
AI-STATS 2003
Observations on the Nystroem Method for Gaussian Process Prediction gzipped postscript: Christopher K. I. Williams, Carl Edward Rasmussen, Anton Schwaighofer, Volker Tresp.
EDI-INF-RR-0120 An Analysis of Contrastive Divergence Learning in Gaussian Boltzmann Machines: Christopher K. I. Williams and Felix V. Agakov
Informatics Research Report, May 2002.
Gaussian Processes gzipped postscript: Christopher K. I. Williams
The Handbook of Brain Theory and Neural Networks, Second edition (M.A. Arbib, Ed.), Cambridge, MA: © The MIT Press, 2002.
Dynamic Trees: Learning to Model Outdoor Scenes gzipped postscript: Nicholas J. Adams, Christopher K. I. Williams
Proceedings of the European Conference on Computer Vision 2002. Lecture Notes in Computer Science. © Springer-Verlag (2002)
Products of Gaussians gzipped postscript: Christopher K. I. Williams, Felix V. Agakov, Stephen N. Felderof
Advances in Neural Information Processing Systems 14 eds. T. G. Diettrich, S. Becker, Z. Ghahramani MIT Press (2002)

2001

EDI-INF-RR-0043 Products of Gaussians and Probabilistic Minor Component Analysis: C. K. I. Williams and F. V. Agakov
Informatics Research Report, July 2001. A shortened version of this report has been published in Neural Computation, 14(5), 1169-1182 (2002).
Comparing Mean Field and Exact EM in Tree Structured Belief Networks gzipped postscript: Nicholas J. Adams, Christopher K. I. Williams and Amos J. Storkey
In Proceedings of Fourth International ICSC Symposium on Soft Computing and Intelligent Systems for Industry, June 2001.
Products and Sums of Tree-Structured Gaussian Processes gzipped postscript: Christopher K. I. Williams and Stephen N. Felderhof
In Proceedings of Fourth International ICSC Symposium on Soft Computing and Intelligent Systems for Industry, June 2001.
Comparing Bayesian Neural Network Algorithms for Classifying Segmented Outdoor Images gzipped postscript: Francesco Vivarelli and Christopher K. I. Williams
Technical report, July 2001. This TR is a close-to-final draft of a paper which was published in Neural Networks 14(4-5) May 2001, 427-437.
Combining belief networks and neural networks for scene segmentation gzipped postscript: Xiaojuan Feng, C. K. I. Williams and S. N. Felderhof
Submitted to IEEE Trans PAMI, April 1999. Revised version March 2001, accepted for publication July 2001. Published in IEEE Trans PAMI 24(4) 467-483 (2002)
IEEE legal stuff This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without explicit permission of the copyright holder.
Software developed for this project is available.
Using the Nystrom Method to Speed Up Kernel Machines gzipped postscript: Christopher K. I. Williams and Matthias Seeger
Advances in Neural Information Processing Systems 13 eds. T. K. Leen, T. G. Diettrich, V. Tresp. MIT Press (2001)
The version above is final, replaces version as submitted to NIPS which was posted on this page 18 June 2000
NB See the later paper "Observations on the Nystroem Method for Gaussian Process Prediction" by Christopher K. I. Williams, Carl Edward Rasmussen, Anton Schwaighofer, Volker Tresp (2002) [available above] for important additional comments on the Nystroem method.
On a Connection between Kernel PCA and Metric Multidimensional Scaling gzipped postscript: Christopher K. I. Williams
Advances in Neural Information Processing Systems 13 eds. T. K. Leen, T. G. Diettrich, V. Tresp. MIT Press (2001)
Dynamic Positional Trees for Structural Image Analysis gzipped postscript: Amos J. Storkey and Christopher K. I. Williams
In: Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics (2001)

2000

The Effect of the Input Density Distribution on Kernel-based Classifiers gzipped postscript: Christopher K. I. Williams and Matthias Seeger
In: Proceedings of the Seventeenth International Conference on Machine Learning. Morgan Kaufmann (2000).
MFDTs: Mean Field Dynamic Trees gzipped postscript: Nicholas J. Adams, Amos J. Storkey, Zoubin Ghahramani and Christopher K. I. Williams
In: Proceedings of 15th International Conference on Pattern Recognition, 2000.
A MCMC approach to Hierarchical Mixture Modelling gzipped postscript: C. K. I. Williams
In Advances in Neural Information Processing Systems 12, eds. S. A. Solla, T. K. Leen and K-R. Muller, MIT Press (2000)

1999

Tree-structured Belief Networks as Models of Images gzipped postscript: C. K. I. Williams and Xiaojuan Feng
In ICANN 99: Artificial Neural Networks
SDTs: Sparse Dynamic Trees gzipped postscript: Nicholas J. Adams and C. K. I. Williams
In ICANN 99: Artificial Neural Networks
Finite-dimensional approximation of Gaussian processes gzipped postscript: Giancarlo Ferrari Trecate and C. K. I. Williams and M. Opper
In: Advances in Neural Information Processing Systems 11, eds. M. J. Kearns, S. A. Solla and D. A. Cohn. MIT Press, 1999.
Discovering hidden features with Gaussian processes regression gzipped postscript: Francesco Vivarelli and C. K. I. Williams
In: Advances in Neural Information Processing Systems 11, eds. M. J. Kearns, S. A. Solla and D. A. Cohn. MIT Press, 1999.
DTs: Dynamic Trees gzipped postscript: C. K. I. Williams and Nicholas J. Adams
In: Advances in Neural Information Processing Systems 11, eds. M. J. Kearns, S. A. Solla and D. A. Cohn. MIT Press, 1999.

1998

NCRG/98/002 Regression with Input-dependent Noise: A Gaussian Process Treatment: P. W. Goldberg and C. K. I. Williams and C. M. Bishop
In Advances in Neural Information Processing Systems 10. Editor: M. I. Jordan and M. J. Kearns and S. A. Solla. MIT Press.
NCRG/98/012 Developments of the Generative Topographic Mapping: C. M. Bishop and M. Svensen and C. K. I. Williams
In Neurocomputing 21 203-224 (1998).
NCRG/98/013 Combining neural networks and belief networks for image segmentation: C. K. I. Williams and X. Feng
In Proc. 1998 IEEE Signal Processing Society Workshop on Neural Networks for Signal Processing. Cambridge, UK, 31 August-3 September 1998.
NCRG/98/014 Training Bayesian networks for image segmentation: X. Feng and C. K. I. Williams
In Proceedings of SPIE vol 3457. Presented at SPIE's 43rd Annual Meeting, San Diego, CA, July 19-24 1998.
NCRG/98/015 Upper and lower bounds on the learning curve for Gaussian processes: Revised version of 24 April 1999 available as gzipped postscipt.
Christopher K. I. Williams and Francesco Vivarelli
Final version appears in Machine Learning, 40(1), 77-102 (2000)
NCRG/98/023 Bayesian Inference for Wind Field Retrieval: Dan Cornford and Ian T. Nabney and Christopher K. I. Williams
Neurocomputing , 30(1-4), 3-11, 1999.
NCRG/98/025 Adding Constrained Discontinuities to Gaussian Process Models of Wind Fields: Dan Cornford and Ian T. Nabney and Christopher K. I. Williams
Advances in Neural Information Processing Systems 11, eds. M. J. Kearns, S. A. Solla and D. A. Cohn.
Bayesian Classification with Gaussian Processes gzipped postscript: C. K. I. Williams and David Barber
In: IEEE Trans Pattern Analysis and Machine Intelligence , 20(12) 1342-1351, (1998).
IEEE legal stuff: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without explicit permission of the copyright holder.
Code is available.

1997

NCRG/97/006 Magnification Factors for the GTM Algorithm: Christopher M. Bishop and Markus Svensen and Christopher K. I. Williams
In Proceedings IEE Fifth International Conference on Artificial Neural Networks.
NCRG/97/007 Using Bayesian neural networks to classify segmented images: Francesco Vivarelli and Christopher K. I. Williams
In Proceedings of the IEE fifth International Conference on Artificial Neural Network s.
NCRG/97/008 Magnification Factors for the SOM and GTM Algorithms: Christopher M. Bishop, Markus Svensen and Christopher K. I. Williams
In Proceedings 1997 Workshop on Self-Organizing Maps, Helsinki, Finland.
NCRG/97/011 Gaussian Regression and Optimal Finite Dimensional Linear Models: Huaiyu Zhu and Christopher K. I. Williams and Richard Rohwer and Michal Morciniec
Aston University. Also appears in C. M. Bishop (editor), Neural Networks and Machine Learning, 1998, Springer-Verlag,
NCRG/97/012 Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond: C. K. I. Williams
Aston University, UK. In "Learning and Inference in Graphical Models", ed. M. I. Jordan, Kluwer, 1998.
NCRG/97/015 Bayesian Classification with Gaussian Processes: Christopher K. I. Williams and David Barber
Neural Computing Research Group, Aston University. Submitted to IEEE PAMI, 16 November 1997.
NCRG/97/025 Computation with infinite neural networks: Christopher K. I. Williams
Neural Computing Research Group, Aston University. Similar to paper published in Neural Computation 10(5), pp 1203-1216, (1998).

1996

NCRG/96/005 An Upper Bound on the Bayesian Error Bars for Generalized Linear Regression: Cazhaow S. Qazaz and Christopher K. I. Williams and Christopher M. Bishop
. In Mathematics of Neural Networks: Models, Algorithms and Applications. Eds. S. W. Ellacott, J. C. Mason, I. J. Anderson. Kluwer, 1977.
NCRG/96/011 EM Optimization of Latent-Variable Density Models: Christopher M. Bishop and M. Svensen and Christopher K. I. Williams
In Advances in Neural Information Processing Systems. Editor: D. S. Touretzky and M. C. Mozer and M. E. Hasselmo. 8. MIT Press, Cambridge, MA.
NCRG/96/013 Gaussian Processes for Regression: C. K. I. Williams and C. E. Rasmussen
In Advances in Neural Information Processing Systems 8. Editor: D. S. Touretzky and M. C. Mozer and M. E. Hasselmo. MIT Press.
NCRG/96/015 GTM: The Generative Topographic Mapping: Christopher M. Bishop and Markus Svensen and Christopher K. I. Williams
Neural Computation 10(1), 215-234 (1998).
Associated software: GTM toolbox
misc96-007 Using generative models for handwritten digit recognition: Revow, M. and Williams, C. K. I. and Hinton, G. E.
IEEE Transactions on Pattern Analysis and Machine Intelligence . 18(6). pp 592-606.
Code is available from Michael Revow's homepage.
NCRG/96/025 Instantiating deformable models with a neural net: C. K. I. Williams and M. Revow and G. E. Hinton
Computer Vision and Image Understanding 68(1) 120-126 (1997).
NCRG/96/026 Computing with infinite networks: C. K. I. Williams
In Advances in Neural Information Processing Systems 9. Editor: M. C. Mozer and M. I. Jordan and T. Petsche. MIT Press, Cambridge, MA.
NCRG/96/027 Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo: D. Barber and C. K. I. Williams
In Advances in Neural Information Processing Systems 9. Editor: M. C. Mozer and M. I. Jordan and T. Petsche. MIT Press, Cambridge, MA.
NCRG/96/030 GTM: A Principled Alternative to the Self-Organizing Map: Christopher M. Bishop, Markus Svensen and Christopher K. I. Williams
In Advances in Neural Information Processing Systems 9. Eds M. C. Mozer, M. I. Jordan and T. Petsche. MIT Press.
NCRG/96/031 GTM: A Principled Alternative to the Self-Organizing Map: Christopher M. Bishop and M. Svensen and Christopher K. I. Williams
In Proceedings 1996 International Conference on Artificial Neural Networks, ICANN'96. Editor: C. von der Malsburg and W. von Seelen and J. C. Vorbruggen and B. Sendhoff. pp 164--170. Springer-Verlag.

1995

NCRG/95/023 Regression with Gaussian Processes: C. K. I. Williams
Paper presented at the Mathematics of Neural Networks and Applications conference, Oxford, UK, July 1995. In Mathematics of Neural Networks: Models, Algorithms and Applications. Eds. S W Ellacott, J C Mason and I J Anderson, Kluwer, 1997.
NCRG/95/024 On the relationship between Bayesian error bars and the input data density: C. K. I. Williams and C. Qazaz and C. M. Bishop and H. Zhu
In Proc. Fourth International Conference on Artificial Neural Networks.
Lending direction to neural networks gzipped postscript: Richard S. Zemel, C. K. I. Williams, and Michael Mozer. Neural Networks, 8(4), pp. 503-512 (1995).

1994

Combining deformable models and neural networks for handprinted digit recognition compressed postscript: C. K. I. Williams
PhD thesis. Department of Computer Science, University of Toronto.

1993

Using mixtures of deformable models to capture variations in hand printed digits gzipped postscript: Michael Revow, Christopher K. I. Williams and Geoffrey E. Hinton
In: Third International workshop on Frontiers in Handwriting Recognition, Buffalo, USA. pp 142-152.

1992

Adaptive elastic models for hand-printed character recognition gzipped postscript: Geoffrey E. Hinton, Christopher K. I. Williams and Michael Revow
In Advances in Neural Information Processing 4, J.E. Moody, S.J. Hanson and R.P Lippman (eds), Morgan Kaufmann, 1992
Learning to Segment Images Using Dynamic Feature Binding pdf: Michael C. Mozer, Richard S. Zemel, Marlene Behrmann, Christopher K. I. Williams
In Neural Computation 4 650-665 (1992).

1990

Mean field networks that learn to discriminate teporally distorted strings pdf: Christopher K. I. Williams and Geoffrey E. Hinton
In: Proceedings of the 1990 Connmectionist Models Summer School, eds D. S. Touretzky, J. L. Elman, T. J. Sejnowski, G. E. Hinton. Morgan Kaufmann (1990).

Chris Williams