Christopher K. I. Williams

Jyri J. Kivinen, Christopher K. I. Williams: Multiple Texture Boltzmann Machines. Journal of Machine Learning Research - Proceedings Track 22: 638-646 (2012)

Andrew Zisserman, John M. Winn, Andrew W. Fitzgibbon, Luc J. Van Gool, Josef Sivic, Christopher K. I. Williams, David Hogg: In Memoriam: Mark Everingham. IEEE Trans. Pattern Anal. Mach. Intell. 34(11): 2081-2082 (2012)

Krzysztof Chalupka, Christopher K. I. Williams, Iain Murray: A Framework for Evaluating Approximation Methods for Gaussian Process Regression. CoRR abs/1205.6326 (2012)

Bill Triggs, Christopher K. I. Williams: Special Issue on Probabilistic Models for Image Understanding, Part II. International Journal of Computer Vision 95(3): 313-314 (2011)

Grigori Fursin, Yuriy Kashnikov, Abdul Wahid Memon, Zbigniew Chamski, Olivier Temam, Mircea Namolaru, Elad Yom-Tov, Bilha Mendelson, Ayal Zaks, Eric Courtois, François Bodin, Phil Barnard, Elton Ashton, Edwin V. Bonilla, John Thomson, Christopher K. I. Williams, Michael F. P. O'Boyle: Milepost GCC: Machine Learning Enabled Self-tuning Compiler. International Journal of Parallel Programming 39(3): 296-327 (2011)

Stefan Harmeling, Christopher K. I. Williams: Greedy Learning of Binary Latent Trees. IEEE Trans. Pattern Anal. Mach. Intell. 33(6): 1087-1097 (2011)

Christopher K. I. Williams, Ioan Stanculescu: Automating the Calibration of a Neonatal Condition Monitoring System. AIME 2011: 240-249

Jyri J. Kivinen, Christopher K. I. Williams: Transformation Equivariant Boltzmann Machines. ICANN (1) 2011: 1-9

Bill Triggs, Christopher K. I. Williams: Editorial: Special Issue on Probabilistic Models for Image Understanding. International Journal of Computer Vision 88(2): 145-146 (2010)

Mark Everingham, Luc J. Van Gool, Christopher K. I. Williams, John M. Winn, Andrew Zisserman: The Pascal Visual Object Classes (VOC) Challenge. International Journal of Computer Vision 88(2): 303-338 (2010)

John D. Lafferty, Christopher K. I. Williams, John Shawe-Taylor, Richard S. Zemel, Aron Culotta (Eds.): Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, Vancouver, British Columbia, Canada. Curran Associates, Inc. 2010

Moray Allan, Christopher K. I. Williams: Object localisation using the Generative Template of Features. Computer Vision and Image Understanding 113(7): 824-838 (2009)

John A. Quinn, Christopher K. I. Williams, Neil McIntosh: Factorial Switching Linear Dynamical Systems Applied to Physiological Condition Monitoring. IEEE Trans. Pattern Anal. Mach. Intell. 31(9): 1537-1551 (2009)

Nicolas Heess, Christopher K. I. Williams, Geoffrey E. Hinton: Learning Generative Texture Models with extended Fields-of-Experts. BMVC 2009: 1-11

Yoshua Bengio, Dale Schuurmans, John D. Lafferty, Christopher K. I. Williams, Aron Culotta (Eds.): Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, Vancouver, British Columbia, Canada. Curran Associates, Inc. 2009, isbn 9781615679119

John A. Quinn, Christopher K. I. Williams: Signal masking in Gaussian channels. ICASSP 2008: 2989-2992

Kian Ming Adam Chai, Christopher K. I. Williams, Stefan Klanke, Sethu Vijayakumar: Multi-task Gaussian Process Learning of Robot Inverse Dynamics. NIPS 2008: 265-272

Edwin V. Bonilla, Felix V. Agakov, Christopher K. I. Williams: Kernel Multi-task Learning using Task-specific Features. Journal of Machine Learning Research - Proceedings Track 2: 43-50 (2007)

John A. Quinn, Christopher K. I. Williams: Known Unknowns: Novelty Detection in Condition Monitoring. IbPRIA (1) 2007: 1-6

Edwin V. Bonilla, Kian Ming Adam Chai, Christopher K. I. Williams: Multi-task Gaussian Process Prediction. NIPS 2007

Wolfgang P. Lehrach, Dirk Husmeier, Christopher K. I. Williams: A regularized discriminative model for the prediction of protein-peptide interactions. Bioinformatics 22(5): 532-540 (2006)

Felix V. Agakov, Edwin V. Bonilla, John Cavazos, Björn Franke, Grigori Fursin, Michael F. P. O'Boyle, John Thomson, Marc Toussaint, Christopher K. I. Williams: Using Machine Learning to Focus Iterative Optimization. CGO 2006: 295-305

Jean Ponce, Tamara L. Berg, Mark Everingham, David A. Forsyth, Martial Hebert, Svetlana Lazebnik, Marcin Marszalek, Cordelia Schmid, Bryan C. Russell, Antonio Torralba, Christopher K. I. Williams, Jianguo Zhang, Andrew Zisserman: Dataset Issues in Object Recognition. Toward Category-Level Object Recognition 2006: 29-48

Michalis K. Titsias, Christopher K. I. Williams: Sequential Learning of Layered Models from Video. Toward Category-Level Object Recognition 2006: 577-595

Edwin V. Bonilla, Christopher K. I. Williams, Felix V. Agakov, John Cavazos, John Thomson, Michael F. P. O'Boyle: Predictive search distributions. ICML 2006: 121-128

Christopher K. I. Williams: How to Pretend That Correlated Variables Are Independent by Using Difference Observations. Neural Computation 17(1): 1-6 (2005)

John Shawe-Taylor, Christopher K. I. Williams, Nello Cristianini, Jaz S. Kandola: On the eigenspectrum of the gram matrix and the generalization error of kernel-PCA. IEEE Transactions on Information Theory 51(7): 2510-2522 (2005)

Moray Allan, Michalis K. Titsias, Christopher K. I. Williams: Fast Learning of Sprites using Invariant Features. BMVC 2005

Mark Everingham, Andrew Zisserman, Christopher K. I. Williams, Luc J. Van Gool, Moray Allan, Christopher M. Bishop, Olivier Chapelle, Navneet Dalal, Thomas Deselaers, Gyuri Dorkó, Stefan Duffner, Jan Eichhorn, Jason D. R. Farquhar, Mario Fritz, Christophe Garcia, Thomas L. Griffiths, Frédéric Jurie, Daniel Keysers, Markus Koskela, Jorma Laaksonen, Diane Larlus, Bastian Leibe, Hongying Meng, Hermann Ney, Bernt Schiele, Cordelia Schmid, Edgar Seemann, John Shawe-Taylor, Amos J. Storkey, Sándor Szedmák, Bill Triggs, Ilkay Ulusoy, Ville Viitaniemi, Jianguo Zhang: The 2005 PASCAL Visual Object Classes Challenge. MLCW 2005: 117-176

Christopher K. I. Williams, John A. Quinn, Neil McIntosh: Factorial Switching Kalman Filters for Condition Monitoring in Neonatal Intensive Care. NIPS 2005

Michalis K. Titsias, Christopher K. I. Williams: Unsupervised Learning of Multiple Aspects of Moving Objects from Video. Panhellenic Conference on Informatics 2005: 746-756

Wolfgang P. Lehrach, Dirk Husmeier, Christopher K. I. Williams: Probabilistic in Silico Prediction of Protein-Peptide Interactions. Systems Biology and Regulatory Genomics 2005: 188-197

Christopher K. I. Williams, Michalis K. Titsias: Greedy Learning of Multiple Objects in Images Using Robust Statistics and Factorial Learning. Neural Computation 16(5): 1039-1062 (2004)

Peter Sollich, Christopher K. I. Williams: Understanding Gaussian Process Regression Using the Equivalent Kernel. Deterministic and Statistical Methods in Machine Learning 2004: 211-228

Moray Allan, Christopher K. I. Williams: Harmonising Chorales by Probabilistic Inference. NIPS 2004

Peter Sollich, Christopher K. I. Williams: Using the Equivalent Kernel to Understand Gaussian Process Regression. NIPS 2004

Nicholas J. Adams, Christopher K. I. Williams: Dynamic trees for image modelling. Image Vision Comput. 21(10): 865-877 (2003)

Amos J. Storkey, Christopher K. I. Williams: Image Modeling with Position-Encoding Dynamic Trees. IEEE Trans. Pattern Anal. Mach. Intell. 25(7): 859-871 (2003)

Max Welling, Felix V. Agakov, Christopher K. I. Williams: Extreme Components Analysis. NIPS 2003

Miguel Á. Carreira-Perpiñán, Christopher K. I. Williams: On the Number of Modes of a Gaussian Mixture. Scale-Space 2003: 625-640

Amos J. Storkey, Nigel C. Hambly, Christopher K. I. Williams, Robert G. Mann: Renewal Strings for Cleaning Astronomical Databases. UAI 2003: 559-566

Christopher K. I. Williams: On a Connection between Kernel PCA and Metric Multidimensional Scaling. Machine Learning 46(1-3): 11-19 (2002)

Christopher K. I. Williams, Felix V. Agakov: Products of Gaussians and Probabilistic Minor Component Analysis. Neural Computation 14(5): 1169-1182 (2002)

Xiaojuan Feng, Christopher K. I. Williams, Stephen N. Felderhof: Combining Belief Networks and Neural Networks for Scene Segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 24(4): 467-483 (2002)

John Shawe-Taylor, Christopher K. I. Williams, Nello Cristianini, Jaz S. Kandola: On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum. ALT 2002: 23-40

John Shawe-Taylor, Christopher K. I. Williams, Nello Cristianini, Jaz S. Kandola: On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum. Discovery Science 2002: 12

Nicholas J. Adams, Christopher K. I. Williams: Dynamic Trees: Learning to Model Outdoor Scenes. ECCV (4) 2002: 82-96

John Shawe-Taylor, Christopher K. I. Williams: The Stability of Kernel Principal Components Analysis and its Relation to the Process Eigenspectrum. NIPS 2002: 367-374

Christopher K. I. Williams, Michalis K. Titsias: Learning About Multiple Objects in Images: Factorial Learning without Factorial Search. NIPS 2002: 1391-1398

Francesco Vivarelli, Christopher K. I. Williams: Comparing Bayesian neural network algorithms for classifying segmented outdoor images. Neural Networks 14(4-5): 427-437 (2001)

Christopher K. I. Williams, Felix V. Agakov, Stephen N. Felderhof: Products of Gaussians. NIPS 2001: 1017-1024

Ian T. Nabney, Dan Cornford, Christopher K. I. Williams: Bayesian inference for wind field retrieval. Neurocomputing 30(1-4): 3-11 (2000)

Christopher K. I. Williams, Francesco Vivarelli: Upper and Lower Bounds on the Learning Curve for Gaussian Processes. Machine Learning 40(1): 77-102 (2000)

Christopher K. I. Williams, Matthias Seeger: The Effect of the Input Density Distribution on Kernel-based Classifiers. ICML 2000: 1159-1166

Nicholas J. Adams, Amos J. Storkey, Christopher K. I. Williams, Zoubin Ghahramani: MFDTs: Mean Field Dynamic Trees. ICPR 2000: 3151-3154

Christopher K. I. Williams: On a Connection between Kernel PCA and Metric Multidimensional Scaling. NIPS 2000: 675-681

Christopher K. I. Williams, Matthias Seeger: Using the Nyström Method to Speed Up Kernel Machines. NIPS 2000: 682-688

Christopher K. I. Williams: A MCMC Approach to Hierarchical Mixture Modelling. NIPS 1999: 680-686

Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams: Developments of the generative topographic mapping. Neurocomputing 21(1-3): 203-224 (1998)

Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams: GTM: The Generative Topographic Mapping. Neural Computation 10(1): 215-234 (1998)

Christopher K. I. Williams: Computation with Infinite Neural Networks. Neural Computation 10(5): 1203-1216 (1998)

Christopher K. I. Williams, David Barber: Bayesian Classification With Gaussian Processes. IEEE Trans. Pattern Anal. Mach. Intell. 20(12): 1342-1351 (1998)

Giancarlo Ferrari-Trecate, Christopher K. I. Williams, Manfred Opper: Finite-Dimensional Approximation of Gaussian Processes. NIPS 1998: 218-224

Francesco Vivarelli, Christopher K. I. Williams: Discovering Hidden Features with Gaussian Processes Regression. NIPS 1998: 613-619

Christopher K. I. Williams, Nicholas J. Adams: DTs: Dynamic Trees. NIPS 1998: 634-640

Dan Cornford, Ian T. Nabney, Christopher K. I. Williams: Adding Constrained Discontinuities to Gaussian Process Models of Wind Fields. NIPS 1998: 861-867

Christopher K. I. Williams, Michael Revow, Geoffrey E. Hinton: Instantiating Deformable Models with a Neural Net. Computer Vision and Image Understanding 68(1): 120-126 (1997)

Paul W. Goldberg, Christopher K. I. Williams, Christopher M. Bishop: Regression with Input-dependent Noise: A Gaussian Process Treatment. NIPS 1997

Michael Revow, Christopher K. I. Williams, Geoffrey E. Hinton: Using Generative Models for Handwritten Digit Recognition. IEEE Trans. Pattern Anal. Mach. Intell. 18(6): 592-606 (1996)

Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams: GTM: A Principled Alternative to the Self-Organizing Map. ICANN 1996: 165-170

Christopher K. I. Williams: Computing with Infinite Networks. NIPS 1996: 295-301

David Barber, Christopher K. I. Williams: Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo. NIPS 1996: 340-346

Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams: GTM: A Principled Alternative to the Self-Organizing Map. NIPS 1996: 354-360

Richard S. Zemel, Christopher K. I. Williams, Michael Mozer: Lending direction to neural networks. Neural Networks 8(4): 503-512 (1995)

Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams: EM Optimization of Latent-Variables Density Models. NIPS 1995: 465-471

Christopher K. I. Williams, Carl Edward Rasmussen: Gaussian Processes for Regression. NIPS 1995: 514-520

Christopher K. I. Williams, Michael Revow, Geoffrey E. Hinton: Using a neural net to instantiate a deformable model. NIPS 1994: 965-972

Michael C. Mozer, Richard S. Zemel, Marlene Behrmann, Christopher K. I. Williams: Learning to Segment Images Using Dynamic Feature Binding. Neural Computation 4(5): 650-665 (1992)

Richard S. Zemel, Christopher K. I. Williams, Michael Mozer: Directional-Unit Boltzmann Machines. NIPS 1992: 172-179

Geoffrey E. Hinton, Christopher K. I. Williams, Michael Revow: Adaptive Elastic Models for Hand-Printed Character Recognition. NIPS 1991: 512-519