| 2008 |
|---|
| 60 | EE | 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 |
| 2007 |
|---|
| 59 | EE | John A. Quinn,
Christopher K. I. Williams:
Known Unknowns: Novelty Detection in Condition Monitoring.
IbPRIA (1) 2007: 1-6 |
| 2006 |
|---|
| 58 | EE | 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 |
| 57 | EE | 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 |
| 56 | EE | Jean Ponce,
Tamara L. Berg,
Mark Everingham,
David A. Forsyth,
Martial Hebert,
Svetlana Lazebnik,
Marcin Marszalek,
Cordelia Schmid,
Bryan C. Russell,
Antonio B. Torralba,
Christopher K. I. Williams,
Jianguo Zhang,
Andrew Zisserman:
Dataset Issues in Object Recognition.
Toward Category-Level Object Recognition 2006: 29-48 |
| 55 | EE | Michalis K. Titsias,
Christopher K. I. Williams:
Sequential Learning of Layered Models from Video.
Toward Category-Level Object Recognition 2006: 577-595 |
| 54 | EE | 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) |
| 2005 |
|---|
| 53 | EE | 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,
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:
The 2005 PASCAL Visual Object Classes Challenge.
MLCW 2005: 117-176 |
| 52 | EE | Christopher K. I. Williams,
John A. Quinn,
Neil McIntosh:
Factorial Switching Kalman Filters for Condition Monitoring in Neonatal Intensive Care.
NIPS 2005 |
| 51 | EE | Michalis K. Titsias,
Christopher K. I. Williams:
Unsupervised Learning of Multiple Aspects of Moving Objects from Video.
Panhellenic Conference on Informatics 2005: 746-756 |
| 50 | EE | 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 |
| 49 | EE | 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) |
| 48 | EE | Christopher K. I. Williams:
How to Pretend That Correlated Variables Are Independent by Using Difference Observations.
Neural Computation 17(1): 1-6 (2005) |
| 2004 |
|---|
| 47 | EE | Peter Sollich,
Christopher K. I. Williams:
Understanding Gaussian Process Regression Using the Equivalent Kernel.
Deterministic and Statistical Methods in Machine Learning 2004: 211-228 |
| 46 | EE | Moray Allan,
Christopher K. I. Williams:
Harmonising Chorales by Probabilistic Inference.
NIPS 2004 |
| 45 | EE | Peter Sollich,
Christopher K. I. Williams:
Using the Equivalent Kernel to Understand Gaussian Process Regression.
NIPS 2004 |
| 44 | EE | 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) |
| 2003 |
|---|
| 43 | EE | Max Welling,
Felix V. Agakov,
Christopher K. I. Williams:
Extreme Components Analysis.
NIPS 2003 |
| 42 | EE | Miguel Á. Carreira-Perpiñán,
Christopher K. I. Williams:
On the Number of Modes of a Gaussian Mixture.
Scale-Space 2003: 625-640 |
| 41 | | Amos J. Storkey,
Nigel C. Hambly,
Christopher K. I. Williams,
Robert G. Mann:
Renewal Strings for Cleaning Astronomical Databases.
UAI 2003: 559-566 |
| 40 | EE | 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) |
| 39 | EE | Nicholas J. Adams,
Christopher K. I. Williams:
Dynamic trees for image modelling.
Image Vision Comput. 21(10): 865-877 (2003) |
| 2002 |
|---|
| 38 | EE | 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 |
| 37 | EE | 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 |
| 36 | EE | Nicholas J. Adams,
Christopher K. I. Williams:
Dynamic Trees: Learning to Model Outdoor Scenes.
ECCV (4) 2002: 82-96 |
| 35 | EE | Christopher K. I. Williams,
Michalis K. Titsias:
Learning About Multiple Objects in Images: Factorial Learning without Factorial Search.
NIPS 2002: 1391-1398 |
| 34 | EE | 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 |
| 33 | EE | 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) |
| 32 | | Christopher K. I. Williams:
On a Connection between Kernel PCA and Metric Multidimensional Scaling.
Machine Learning 46(1-3): 11-19 (2002) |
| 31 | EE | Christopher K. I. Williams,
Felix V. Agakov:
Products of Gaussians and Probabilistic Minor Component Analysis.
Neural Computation 14(5): 1169-1182 (2002) |
| 2001 |
|---|
| 30 | EE | Christopher K. I. Williams,
Felix V. Agakov,
Stephen N. Felderhof:
Products of Gaussians.
NIPS 2001: 1017-1024 |
| 29 | EE | Francesco Vivarelli,
Christopher K. I. Williams:
Comparing Bayesian neural network algorithms for classifying segmented outdoor images.
Neural Networks 14(4-5): 427-437 (2001) |
| 2000 |
|---|
| 28 | | Christopher K. I. Williams,
Matthias Seeger:
The Effect of the Input Density Distribution on Kernel-based Classifiers.
ICML 2000: 1159-1166 |
| 27 | EE | Nicholas J. Adams,
Amos J. Storkey,
Christopher K. I. Williams,
Zoubin Ghahramani:
MFDTs: Mean Field Dynamic Trees.
ICPR 2000: 3151-3154 |
| 26 | | Christopher K. I. Williams:
On a Connection between Kernel PCA and Metric Multidimensional Scaling.
NIPS 2000: 675-681 |
| 25 | | Christopher K. I. Williams,
Matthias Seeger:
Using the Nyström Method to Speed Up Kernel Machines.
NIPS 2000: 682-688 |
| 24 | | Christopher K. I. Williams,
Francesco Vivarelli:
Upper and Lower Bounds on the Learning Curve for Gaussian Processes.
Machine Learning 40(1): 77-102 (2000) |
| 23 | EE | Ian T. Nabney,
Dan Cornford,
Christopher K. I. Williams:
Bayesian inference for wind field retrieval.
Neurocomputing 30(1-4): 3-11 (2000) |
| 1999 |
|---|
| 22 | EE | Christopher K. I. Williams:
A MCMC Approach to Hierarchical Mixture Modelling.
NIPS 1999: 680-686 |
| 1998 |
|---|
| 21 | EE | Giancarlo Ferrari-Trecate,
Christopher K. I. Williams,
Manfred Opper:
Finite-Dimensional Approximation of Gaussian Processes.
NIPS 1998: 218-224 |
| 20 | EE | Francesco Vivarelli,
Christopher K. I. Williams:
Discovering Hidden Features with Gaussian Processes Regression.
NIPS 1998: 613-619 |
| 19 | EE | Christopher K. I. Williams,
Nicholas J. Adams:
DTs: Dynamic Trees.
NIPS 1998: 634-640 |
| 18 | EE | Dan Cornford,
Ian T. Nabney,
Christopher K. I. Williams:
Adding Constrained Discontinuities to Gaussian Process Models of Wind Fields.
NIPS 1998: 861-867 |
| 17 | EE | Christopher K. I. Williams,
David Barber:
Bayesian Classification With Gaussian Processes.
IEEE Trans. Pattern Anal. Mach. Intell. 20(12): 1342-1351 (1998) |
| 16 | | Christopher M. Bishop,
Markus Svensén,
Christopher K. I. Williams:
GTM: The Generative Topographic Mapping.
Neural Computation 10(1): 215-234 (1998) |
| 15 | | Christopher K. I. Williams:
Computation with Infinite Neural Networks.
Neural Computation 10(5): 1203-1216 (1998) |
| 14 | EE | Christopher M. Bishop,
Markus Svensén,
Christopher K. I. Williams:
Developments of the generative topographic mapping.
Neurocomputing 21(1-3): 203-224 (1998) |
| 1997 |
|---|
| 13 | | Paul W. Goldberg,
Christopher K. I. Williams,
Christopher M. Bishop:
Regression with Input-dependent Noise: A Gaussian Process Treatment.
NIPS 1997 |
| 12 | EE | 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) |
| 1996 |
|---|
| 11 | | Christopher M. Bishop,
Markus Svensén,
Christopher K. I. Williams:
GTM: A Principled Alternative to the Self-Organizing Map.
ICANN 1996: 165-170 |
| 10 | EE | Christopher K. I. Williams:
Computing with Infinite Networks.
NIPS 1996: 295-301 |
| 9 | EE | David Barber,
Christopher K. I. Williams:
Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo.
NIPS 1996: 340-346 |
| 8 | EE | Christopher M. Bishop,
Markus Svensén,
Christopher K. I. Williams:
GTM: A Principled Alternative to the Self-Organizing Map.
NIPS 1996: 354-360 |
| 7 | EE | 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) |
| 1995 |
|---|
| 6 | EE | Christopher M. Bishop,
Markus Svensén,
Christopher K. I. Williams:
EM Optimization of Latent-Variables Density Models.
NIPS 1995: 465-471 |
| 5 | EE | Christopher K. I. Williams,
Carl Edward Rasmussen:
Gaussian Processes for Regression.
NIPS 1995: 514-520 |
| 4 | EE | Richard S. Zemel,
Christopher K. I. Williams,
Michael Mozer:
Lending direction to neural networks.
Neural Networks 8(4): 503-512 (1995) |
| 1994 |
|---|
| 3 | EE | Christopher K. I. Williams,
Michael Revow,
Geoffrey E. Hinton:
Using a neural net to instantiate a deformable model.
NIPS 1994: 965-972 |
| 1992 |
|---|
| 2 | EE | Richard S. Zemel,
Christopher K. I. Williams,
Michael Mozer:
Directional-Unit Boltzmann Machines.
NIPS 1992: 172-179 |
| 1991 |
|---|
| 1 | EE | Geoffrey E. Hinton,
Christopher K. I. Williams,
Michael Revow:
Adaptive Elastic Models for Hand-Printed Character Recognition.
NIPS 1991: 512-519 |
