Christopher K. I. Williams

According to our database, Christopher K. I. Williams authored at least 131 papers between 1991 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

AI Assistants: A Framework for Semi-Automated Data Wrangling.

Tomas Petricek, Gerrit J. J. van den Burg, Alfredo Nazábal, Taha Ceritli, Ernesto Jiménez-Ruiz, Christopher K. I. Williams

IEEE Trans. Knowl. Data Eng., September, 2023

Persistent animal identification leveraging non-visual markers.

Michael P. J. Camilleri, Li Zhang, Rasneer S. Bains, Andrew Zisserman, Christopher K. I. Williams

Mach. Vis. Appl., July, 2023

Inference and Learning for Generative Capsule Models.

Alfredo Nazábal, Nikolaos Tsagkas, Christopher K. I. Williams

Neural Comput., 2023

The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence.

Hector Zenil, Jesper Tegnér, Felipe S. Abrahão, Alexander Lavin, Vipin Kumar, Jeremy G. Frey, Adrian Weller, Larisa N. Soldatova, Alan R. Bundy, Nicholas R. Jennings, Koichi Takahashi, Lawrence Hunter, Saso Dzeroski, Andrew Briggs, Frederick D. Gregory, Carla P. Gomes, Christopher K. I. Williams, Jon Rowe, James A. Evans, Hiroaki Kitano, Joshua B. Tenenbaum, Ross D. King

CoRR, 2023

Of Mice and Mates: Automated Classification and Modelling of Mouse Behaviour in Groups using a Single Model across Cages.

Michael P. J. Camilleri, Rasneer S. Bains, Christopher K. I. Williams

CoRR, 2023

Structured Generative Models for Scene Understanding.

Christopher K. I. Williams

CoRR, 2023

2022

On Suspicious Coincidences and Pointwise Mutual Information.

Christopher K. I. Williams

Neural Comput., 2022

Multi-Task Dynamical Systems.

Alex Bird, Christopher K. I. Williams, Christopher Hawthorne

J. Mach. Learn. Res., 2022

The Elliptical Quartic Exponential Distribution: An Annular Distribution Obtained via Maximum Entropy.

Christopher K. I. Williams

CoRR, 2022

Align-Deform-Subtract: An Interventional Framework for Explaining Object Differences.

Cian Eastwood, Nanbo Li, Christopher K. I. Williams

CoRR, 2022

Automating data science.

Tijl De Bie, Luc De Raedt, José Hernández-Orallo, Holger H. Hoos, Padhraic Smyth, Christopher K. I. Williams

Commun. ACM, 2022

Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration.

Cian Eastwood, Ian Mason, Christopher K. I. Williams, Bernhard Schölkopf

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

The Effect of Class Imbalance on Precision-Recall Curves.

Christopher K. I. Williams

Neural Comput., 2021

Tracking and Long-Term Identification Using Non-Visual Markers.

Michael P. J. Camilleri, Li Zhang, Rasneer S. Bains, Andrew Zisserman, Christopher K. I. Williams

CoRR, 2021

Identifying the Units of Measurement in Tabular Data.

Taha Ceritli, Christopher K. I. Williams

CoRR, 2021

ptype-cat: Inferring the Type and Values of Categorical Variables.

Taha Ceritli, Christopher K. I. Williams

CoRR, 2021

Automating Data Science: Prospects and Challenges.

Tijl De Bie, Luc De Raedt, José Hernández-Orallo, Holger H. Hoos, Padhraic Smyth, Christopher K. I. Williams

CoRR, 2021

Inference for Generative Capsule Models.

Alfredo Nazábal, Christopher K. I. Williams

CoRR, 2021

On Memorization in Probabilistic Deep Generative Models.

Gerrit J. J. van den Burg, Christopher K. I. Williams

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Unit-level surprise in neural networks.

Cian Eastwood, Ian Mason, Christopher K. I. Williams

Proceedings of the I (Still) Can't Believe It's Not Better! Workshop at NeurIPS 2021, 2021

2020

Learning Direct Optimization for scene understanding.

Lukasz Romaszko, Christopher K. I. Williams, John M. Winn

Pattern Recognit., 2020

ptype: probabilistic type inference.

Taha Ceritli, Christopher K. I. Williams, James Geddes

Data Min. Knowl. Discov., 2020

VAEs in the Presence of Missing Data.

Mark Collier, Alfredo Nazábal, Christopher K. I. Williams

CoRR, 2020

Data Engineering for Data Analytics: A Classification of the Issues, and Case Studies.

Alfredo Nazábal, Christopher K. I. Williams, Giovanni Colavizza, Camila Rangel Smith, Angus Williams

CoRR, 2020

An Evaluation of Change Point Detection Algorithms.

Gerrit J. J. van den Burg, Christopher K. I. Williams

CoRR, 2020

Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data.

Simão Eduardo, Alfredo Nazábal, Christopher K. I. Williams, Charles Sutton

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Customizing Sequence Generation with Multi-Task Dynamical Systems.

Alex Bird, Christopher K. I. Williams

CoRR, 2019

Robust Variational Autoencoders for Outlier Detection in Mixed-Type Data.

Simão Eduardo, Alfredo Nazábal, Christopher K. I. Williams, Charles Sutton

CoRR, 2019

The Extended Dawid-Skene Model: Fusing Information from Multiple Data Schemas.

Michael P. J. Camilleri, Christopher K. I. Williams

CoRR, 2019

Inverting Supervised Representations with Autoregressive Neural Density Models.

Charlie Nash, Nate Kushman, Christopher K. I. Williams

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Multi-Task Time Series Analysis applied to Drug Response Modelling.

Alex Bird, Christopher K. I. Williams, Christopher Hawthorne

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Estimating Bacterial and Cellular Load in FCFM Imaging.

Sohan Seth, Ahsan R. Akram, Kevin Dhaliwal, Christopher K. I. Williams

J. Imaging, 2018

Autoencoders and Probabilistic Inference with Missing Data: An Exact Solution for The Factor Analysis Case.

Christopher K. I. Williams, Charlie Nash

CoRR, 2018

A Framework for the Quantitative Evaluation of Disentangled Representations.

Cian Eastwood, Christopher K. I. Williams

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

The shape variational autoencoder: A deep generative model of part-segmented 3D objects.

Charlie Nash, Christopher K. I. Williams

Comput. Graph. Forum, 2017

Estimating Bacterial Load in FCFM Imaging.

Sohan Seth, Ahsan R. Akram, Kevin Dhaliwal, Christopher K. I. Williams

Proceedings of the Medical Image Understanding and Analysis - 21st Annual Conference, 2017

Vision-as-Inverse-Graphics: Obtaining a Rich 3D Explanation of a Scene from a Single Image.

Lukasz Romaszko, Christopher K. I. Williams, Pol Moreno, Pushmeet Kohli

Proceedings of the 2017 IEEE International Conference on Computer Vision Workshops, 2017

2016

Predicting Patient State-of-Health using Sliding Window and Recurrent Classifiers.

Adam McCarthy, Christopher K. I. Williams

CoRR, 2016

Input-Output Non-Linear Dynamical Systems applied to Physiological Condition Monitoring.

Konstantinos Georgatzis, Christopher K. I. Williams, Christopher Hawthorne

Proceedings of the 1st Machine Learning in Health Care, 2016

Overcoming Occlusion with Inverse Graphics.

Pol Moreno, Christopher K. I. Williams, Charlie Nash, Pushmeet Kohli

Proceedings of the Computer Vision - ECCV 2016 Workshops, 2016

2015

The Pascal Visual Object Classes Challenge: A Retrospective.

Mark Everingham, S. M. Ali Eslami, Luc Van Gool, Christopher K. I. Williams, John M. Winn, Andrew Zisserman

Int. J. Comput. Vis., 2015

Tree-Cut for Probabilistic Image Segmentation.

Shell Xu Hu, Christopher K. I. Williams, Sinisa Todorovic

CoRR, 2015

Discriminative Switching Linear Dynamical Systems applied to Physiological Condition Monitoring.

Konstantinos Georgatzis, Christopher K. I. Williams

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

2014

Autoregressive Hidden Markov Models for the Early Detection of Neonatal Sepsis.

Ioan Stanculescu, Christopher K. I. Williams, Yvonne Freer

IEEE J. Biomed. Health Informatics, 2014

The Shape Boltzmann Machine: A Strong Model of Object Shape.

S. M. Ali Eslami, Nicolas Heess, Christopher K. I. Williams, John M. Winn

Int. J. Comput. Vis., 2014

A Hierarchical Switching Linear Dynamical System Applied to the Detection of Sepsis in Neonatal Condition Monitoring.

Ioan Stanculescu, Christopher K. I. Williams, Yvonne Freer

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection.

Jyri J. Kivinen, Christopher K. I. Williams, Nicolas Heess

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

A framework for evaluating approximation methods for Gaussian process regression.

Krzysztof Chalupka, Christopher K. I. Williams, Iain Murray

J. Mach. Learn. Res., 2013

Dictionary of Computer Vision and Image Processing, Second Edition.

Robert B. Fisher, Toby P. Breckon, Kenneth M. Dawson-Howe, Andrew W. Fitzgibbon, Craig Robertson, Emanuele Trucco, Christopher K. I. Williams

Wiley, ISBN: 978-1-119-94186-6, 2013

2012

In Memoriam: Mark Everingham.

Andrew Zisserman, John M. Winn, Andrew W. Fitzgibbon, Luc Van Gool, Josef Sivic, Christopher K. I. Williams, David C. Hogg

IEEE Trans. Pattern Anal. Mach. Intell., 2012

Multiple Texture Boltzmann Machines.

Jyri J. Kivinen, Christopher K. I. Williams

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

A Generative Model for Parts-based Object Segmentation.

S. M. Ali Eslami, Christopher K. I. Williams

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011

Greedy Learning of Binary Latent Trees.

Stefan Harmeling, Christopher K. I. Williams

IEEE Trans. Pattern Anal. Mach. Intell., 2011

Milepost GCC: Machine Learning Enabled Self-tuning Compiler.

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

Int. J. Parallel Program., 2011

Special Issue on Probabilistic Models for Image Understanding, Part II.

Bill Triggs, Christopher K. I. Williams

Int. J. Comput. Vis., 2011

Transformation Equivariant Boltzmann Machines.

Jyri J. Kivinen, Christopher K. I. Williams

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2011, 2011

Factored Shapes and Appearances for Parts-based Object Understanding.

Seyed Mohammadali Eslami, Christopher K. I. Williams

Proceedings of the British Machine Vision Conference, 2011

Automating the Calibration of a Neonatal Condition Monitoring System.

Christopher K. I. Williams, Ioan Stanculescu

Proceedings of the Artificial Intelligence in Medicine, 2011

2010

Editorial: Special Issue on Probabilistic Models for Image Understanding.

Bill Triggs, Christopher K. I. Williams

Int. J. Comput. Vis., 2010

The Pascal Visual Object Classes (VOC) Challenge.

Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John M. Winn, Andrew Zisserman

Int. J. Comput. Vis., 2010

2009

Factorial Switching Linear Dynamical Systems Applied to Physiological Condition Monitoring.

John A. Quinn, Christopher K. I. Williams, Neil McIntosh

IEEE Trans. Pattern Anal. Mach. Intell., 2009

Object localisation using the Generative Template of Features.

Moray Allan, Christopher K. I. Williams

Comput. Vis. Image Underst., 2009

Learning Generative Texture Models with extended Fields-of-Experts.

Nicolas Heess, Christopher K. I. Williams, Geoffrey E. Hinton

Proceedings of the British Machine Vision Conference, 2009

2008

Multi-task Gaussian Process Learning of Robot Inverse Dynamics.

Kian Ming Adam Chai, Christopher K. I. Williams, Stefan Klanke, Sethu Vijayakumar

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Signal masking in Gaussian channels.

John A. Quinn, Christopher K. I. Williams

Proceedings of the IEEE International Conference on Acoustics, 2008

2007

Kernel Multi-task Learning using Task-specific Features.

Edwin V. Bonilla, Felix V. Agakov, Christopher K. I. Williams

Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

Multi-task Gaussian Process Prediction.

Edwin V. Bonilla, Kian Ming Adam Chai, Christopher K. I. Williams

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Known Unknowns: Novelty Detection in Condition Monitoring.

John A. Quinn, Christopher K. I. Williams

Proceedings of the Pattern Recognition and Image Analysis, Third Iberian Conference, 2007

2006

A regularized discriminative model for the prediction of protein-peptide interactions.

Wolfgang P. Lehrach, Dirk Husmeier, Christopher K. I. Williams

Bioinform., 2006

Predictive search distributions.

Edwin V. Bonilla, Christopher K. I. Williams, Felix V. Agakov, John Cavazos, John Thomson, Michael F. P. O'Boyle

Proceedings of the Machine Learning, 2006

Sequential Learning of Layered Models from Video.

Michalis K. Titsias, Christopher K. I. Williams

Proceedings of the Toward Category-Level Object Recognition, 2006

Dataset Issues in Object Recognition.

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

Proceedings of the Toward Category-Level Object Recognition, 2006

Using Machine Learning to Focus Iterative Optimization.

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

Proceedings of the Fourth IEEE/ACM International Symposium on Code Generation and Optimization (CGO 2006), 2006

Gaussian processes for machine learning.

Carl Edward Rasmussen, Christopher K. I. Williams

Adaptive computation and machine learning, MIT Press, ISBN: 026218253X, 2006

2005

On the eigenspectrum of the gram matrix and the generalization error of kernel-PCA.

John Shawe-Taylor, Christopher K. I. Williams, Nello Cristianini, Jaz S. Kandola

IEEE Trans. Inf. Theory, 2005

How to Pretend That Correlated Variables Are Independent by Using Difference Observations.

Christopher K. I. Williams

Neural Comput., 2005

Probabilistic <i>in Silico</i> Prediction of Protein-Peptide Interactions.

Wolfgang P. Lehrach, Dirk Husmeier, Christopher K. I. Williams

Proceedings of the Systems Biology and Regulatory Genomics, 2005

Unsupervised Learning of Multiple Aspects of Moving Objects from Video.

Michalis K. Titsias, Christopher K. I. Williams

Proceedings of the Advances in Informatics, 2005

Factorial Switching Kalman Filters for Condition Monitoring in Neonatal Intensive Care.

Christopher K. I. Williams, John A. Quinn, Neil McIntosh

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

The 2005 PASCAL Visual Object Classes Challenge.

Mark Everingham, Andrew Zisserman, Christopher K. I. Williams, Luc 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, Tom 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

Proceedings of the Machine Learning Challenges, 2005

Fast Learning of Sprites using Invariant Features.

Moray Allan, Michalis K. Titsias, Christopher K. I. Williams

Proceedings of the British Machine Vision Conference 2005, Oxford, UK, September 2005, 2005

2004

Greedy Learning of Multiple Objects in Images Using Robust Statistics and Factorial Learning.

Christopher K. I. Williams, Michalis K. Titsias

Neural Comput., 2004

Using the Equivalent Kernel to Understand Gaussian Process Regression.

Peter Sollich, Christopher K. I. Williams

Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

Harmonising Chorales by Probabilistic Inference.

Moray Allan, Christopher K. I. Williams

Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

Understanding Gaussian Process Regression Using the Equivalent Kernel.

Peter Sollich, Christopher K. I. Williams

Proceedings of the Deterministic and Statistical Methods in Machine Learning, 2004

Fast Unsupervised Greedy Learning of Multiple Objects and Parts from Video.

Michalis K. Titsias, Christopher K. I. Williams

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2004

2003

Image Modeling with Position-Encoding Dynamic Trees.

Amos J. Storkey, Christopher K. I. Williams

IEEE Trans. Pattern Anal. Mach. Intell., 2003

Dynamic trees for image modelling.

Nicholas J. Adams, Christopher K. I. Williams

Image Vis. Comput., 2003

Renewal Strings for Cleaning Astronomical Databases.

Amos J. Storkey, Nigel C. Hambly, Christopher K. I. Williams, Robert G. Mann

Proceedings of the UAI '03, 2003

On the Number of Modes of a Gaussian Mixture.

Miguel Á. Carreira-Perpiñán, Christopher K. I. Williams

Proceedings of the Scale Space Methods in Computer Vision, 4th International Conference, 2003

Extreme Components Analysis.

Max Welling, Felix V. Agakov, Christopher K. I. Williams

Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Fast Forward Selection to Speed Up Sparse Gaussian Process Regression.

Matthias W. Seeger, Christopher K. I. Williams, Neil D. Lawrence

Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003

2002

Combining Belief Networks and Neural Networks for Scene Segmentation.

Xiaojuan Feng, Christopher K. I. Williams, Stephen N. Felderhof

IEEE Trans. Pattern Anal. Mach. Intell., 2002

Products of Gaussians and Probabilistic Minor Component Analysis.

Christopher K. I. Williams, Felix V. Agakov

Neural Comput., 2002

On a Connection between Kernel PCA and Metric Multidimensional Scaling.

Christopher K. I. Williams

Mach. Learn., 2002

Learning About Multiple Objects in Images: Factorial Learning without Factorial Search.

Christopher K. I. Williams, Michalis K. Titsias

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

The Stability of Kernel Principal Components Analysis and its Relation to the Process Eigenspectrum.

John Shawe-Taylor, Christopher K. I. Williams

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

Dynamic Trees: Learning to Model Outdoor Scenes.

Nicholas J. Adams, Christopher K. I. Williams

Proceedings of the Computer Vision, 2002

On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum.

John Shawe-Taylor, Christopher K. I. Williams, Nello Cristianini, Jaz S. Kandola

Proceedings of the Discovery Science, 5th International Conference, 2002

2001

Comparing Bayesian neural network algorithms for classifying segmented outdoor images.

Francesco Vivarelli, Christopher K. I. Williams

Neural Networks, 2001

Products of Gaussians.

Christopher K. I. Williams, Felix V. Agakov, Stephen N. Felderhof

Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

Dynamic Positional Trees for Structural Image Analysis.

Amos J. Storkey, Christopher K. I. Williams

Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001

2000

Upper and Lower Bounds on the Learning Curve for Gaussian Processes.

Christopher K. I. Williams, Francesco Vivarelli

Mach. Learn., 2000

Bayesian inference for wind field retrieval.

Ian T. Nabney, Dan Cornford, Christopher K. I. Williams

Neurocomputing, 2000

Using the Nyström Method to Speed Up Kernel Machines.

Christopher K. I. Williams, Matthias W. Seeger

Proceedings of the Advances in Neural Information Processing Systems 13, 2000

MFDTs: Mean Field Dynamic Trees.

Nicholas J. Adams, Amos J. Storkey, Christopher K. I. Williams, Zoubin Ghahramani

Proceedings of the 15th International Conference on Pattern Recognition, 2000

The Effect of the Input Density Distribution on Kernel-based Classifiers.

Christopher K. I. Williams, Matthias W. Seeger

Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29, 2000

1999

A MCMC Approach to Hierarchical Mixture Modelling.

Christopher K. I. Williams

Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

1998

Bayesian Classification With Gaussian Processes.

Christopher K. I. Williams, David Barber

IEEE Trans. Pattern Anal. Mach. Intell., 1998

Computation with Infinite Neural Networks.

Christopher K. I. Williams

Neural Comput., 1998

GTM: The Generative Topographic Mapping.

Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams

Neural Comput., 1998

Developments of the generative topographic mapping.

Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams

Neurocomputing, 1998

DTs: Dynamic Trees.

Christopher K. I. Williams, Nicholas J. Adams

Proceedings of the Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30, 1998

Discovering Hidden Features with Gaussian Processes Regression.

Francesco Vivarelli, Christopher K. I. Williams

Proceedings of the Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30, 1998

Finite-Dimensional Approximation of Gaussian Processes.

Giancarlo Ferrari-Trecate, Christopher K. I. Williams, Manfred Opper

Proceedings of the Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30, 1998

Adding Constrained Discontinuities to Gaussian Process Models of Wind Fields.

Dan Cornford, Ian T. Nabney, Christopher K. I. Williams

Proceedings of the Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30, 1998

Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond.

Christopher K. I. Williams

Proceedings of the Learning in Graphical Models, 1998

1997

Instantiating Deformable Models with a Neural Net.

Christopher K. I. Williams, Michael Revow, Geoffrey E. Hinton

Comput. Vis. Image Underst., 1997

Regression with Input-dependent Noise: A Gaussian Process Treatment.

Paul W. Goldberg, Christopher K. I. Williams, Christopher M. Bishop

Proceedings of the Advances in Neural Information Processing Systems 10, 1997

1996

Using Generative Models for Handwritten Digit Recognition.

Michael Revow, Christopher K. I. Williams, Geoffrey E. Hinton

IEEE Trans. Pattern Anal. Mach. Intell., 1996

Computing with Infinite Networks.

Christopher K. I. Williams

Proceedings of the Advances in Neural Information Processing Systems 9, 1996

GTM: A Principled Alternative to the Self-Organizing Map.

Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams

Proceedings of the Advances in Neural Information Processing Systems 9, 1996

Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo.

David Barber, Christopher K. I. Williams

Proceedings of the Advances in Neural Information Processing Systems 9, 1996

1995

Lending direction to neural networks.

Richard S. Zemel, Christopher K. I. Williams, Michael Mozer

Neural Networks, 1995

Gaussian Processes for Regression.

Christopher K. I. Williams, Carl Edward Rasmussen

Proceedings of the Advances in Neural Information Processing Systems 8, 1995

EM Optimization of Latent-Variables Density Models.

Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams

Proceedings of the Advances in Neural Information Processing Systems 8, 1995

1994

Combining deformable models and neural networks for handprinted digit recognition.

Christopher K. I. Williams

PhD thesis, 1994

Using a neural net to instantiate a deformable model.

Christopher K. I. Williams, Michael Revow, Geoffrey E. Hinton

Proceedings of the Advances in Neural Information Processing Systems 7, 1994

1992

Learning to Segment Images Using Dynamic Feature Binding.

Michael C. Mozer, Richard S. Zemel, Marlene Behrmann, Christopher K. I. Williams

Neural Comput., 1992

Directional-Unit Boltzmann Machines.

Richard S. Zemel, Christopher K. I. Williams, Michael Mozer

Proceedings of the Advances in Neural Information Processing Systems 5, [NIPS Conference, Denver, Colorado, USA, November 30, 1992

1991

Adaptive Elastic Models for Hand-Printed Character Recognition.

Geoffrey E. Hinton, Christopher K. I. Williams, Michael Revow

Proceedings of the Advances in Neural Information Processing Systems 4, 1991