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Christopher K. I. Williams
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- affiliation: University of Edinburgh, Scotland, UK
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2020 – today
- 2024
- [j44]Michael P. J. Camilleri
, Rasneer S. Bains, Christopher K. I. Williams:
Of Mice and Mates: Automated Classification and Modelling of Mouse Behaviour in Groups Using a Single Model Across Cages. Int. J. Comput. Vis. 132(12): 5491-5513 (2024) - [i35]Christopher K. I. Williams:
Naive Bayes Classifiers and One-hot Encoding of Categorical Variables. CoRR abs/2404.18190 (2024) - 2023
- [j43]Michael P. J. Camilleri
Persistent animal identification leveraging non-visual markers. Mach. Vis. Appl. 34(4): 68 (2023) - [j42]Alfredo Nazábal, Nikolaos Tsagkas, Christopher K. I. Williams:
Inference and Learning for Generative Capsule Models. Neural Comput. 35(4): 727-761 (2023) - [j41]Tomas Petricek
AI Assistants: A Framework for Semi-Automated Data Wrangling. IEEE Trans. Knowl. Data Eng. 35(9): 9295-9306 (2023) - [i34]Christopher K. I. Williams:
Structured Generative Models for Scene Understanding. CoRR abs/2302.03531 (2023) - [i33]Michael P. J. Camilleri, Rasneer S. Bains, Christopher K. I. Williams:
Of Mice and Mates: Automated Classification and Modelling of Mouse Behaviour in Groups using a Single Model across Cages. CoRR abs/2306.03066 (2023) - [i32]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:
The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence. CoRR abs/2307.07522 (2023) - 2022
- [j40]Tijl De Bie, Luc De Raedt
Automating data science. Commun. ACM 65(3): 76-87 (2022) - [j39]Alex Bird, Christopher K. I. Williams, Christopher Hawthorne:
Multi-Task Dynamical Systems. J. Mach. Learn. Res. 23: 230:1-230:52 (2022) - [j38]Christopher K. I. Williams:
On Suspicious Coincidences and Pointwise Mutual Information. Neural Comput. 34(10): 2037-2046 (2022) - [c68]Cian Eastwood, Ian Mason, Christopher K. I. Williams, Bernhard Schölkopf:
Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration. ICLR 2022 - [i31]Cian Eastwood, Nanbo Li, Christopher K. I. Williams:
Align-Deform-Subtract: An Interventional Framework for Explaining Object Differences. CoRR abs/2203.04694 (2022) - [i30]Christopher K. I. Williams:
On Suspicious Coincidences and Pointwise Mutual Information. CoRR abs/2203.08089 (2022) - [i29]Alfredo Nazábal, Nikolaos Tsagkas, Christopher K. I. Williams:
Inference and Learning for Generative Capsule Models. CoRR abs/2209.03115 (2022) - [i28]Alex Bird, Christopher K. I. Williams, Christopher Hawthorne:
Multi-Task Dynamical Systems. CoRR abs/2210.04023 (2022) - [i27]Christopher K. I. Williams:
The Elliptical Quartic Exponential Distribution: An Annular Distribution Obtained via Maximum Entropy. CoRR abs/2210.04221 (2022) - [i26]Tomas Petricek, Gerrit J. J. van den Burg, Alfredo Nazábal, Taha Ceritli, Ernesto Jiménez-Ruiz, Christopher K. I. Williams:
AI Assistants: A Framework for Semi-Automated Data Wrangling. CoRR abs/2211.00192 (2022) - 2021
- [j37]Christopher K. I. Williams:
The Effect of Class Imbalance on Precision-Recall Curves. Neural Comput. 33(4): 853-857 (2021) - [c67]Cian Eastwood, Ian Mason, Christopher K. I. Williams:
Unit-level surprise in neural networks. ICBINB@NeurIPS 2021: 33-40 - [c66]Gerrit J. J. van den Burg, Christopher K. I. Williams:
On Memorization in Probabilistic Deep Generative Models. NeurIPS 2021: 27916-27928 - [i25]Alfredo Nazábal, Christopher K. I. Williams:
Inference for Generative Capsule Models. CoRR abs/2103.06676 (2021) - [i24]Tijl De Bie, Luc De Raedt, José Hernández-Orallo, Holger H. Hoos, Padhraic Smyth, Christopher K. I. Williams:
Automating Data Science: Prospects and Challenges. CoRR abs/2105.05699 (2021) - [i23]Gerrit J. J. van den Burg, Christopher K. I. Williams:
On Memorization in Probabilistic Deep Generative Models. CoRR abs/2106.03216 (2021) - [i22]Cian Eastwood, Ian Mason, Christopher K. I. Williams, Bernhard Schölkopf:
Source-Free Adaptation to Measurement Shift via Bottom-Up Feature Restoration. CoRR abs/2107.05446 (2021) - [i21]Taha Ceritli, Christopher K. I. Williams:
ptype-cat: Inferring the Type and Values of Categorical Variables. CoRR abs/2111.11956 (2021) - [i20]Taha Ceritli, Christopher K. I. Williams:
Identifying the Units of Measurement in Tabular Data. CoRR abs/2111.11959 (2021) - [i19]Michael P. J. Camilleri, Li Zhang, Rasneer S. Bains, Andrew Zisserman, Christopher K. I. Williams:
Tracking and Long-Term Identification Using Non-Visual Markers. CoRR abs/2112.06809 (2021) - 2020
- [j36]Taha Ceritli
ptype: probabilistic type inference. Data Min. Knowl. Discov. 34(3): 870-904 (2020) - [j35]Lukasz Romaszko, Christopher K. I. Williams, John M. Winn:
Learning Direct Optimization for scene understanding. Pattern Recognit. 105: 107369 (2020) - [c65]Simão Eduardo, Alfredo Nazábal, Christopher K. I. Williams, Charles Sutton:
Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type Data. AISTATS 2020: 4056-4066 - [i18]Gerrit J. J. van den Burg, Christopher K. I. Williams:
An Evaluation of Change Point Detection Algorithms. CoRR abs/2003.06222 (2020) - [i17]Alfredo Nazábal, Christopher K. I. Williams, Giovanni Colavizza, Camila Rangel Smith, Angus Williams:
Data Engineering for Data Analytics: A Classification of the Issues, and Case Studies. CoRR abs/2004.12929 (2020) - [i16]Mark Collier, Alfredo Nazábal, Christopher K. I. Williams:
VAEs in the Presence of Missing Data. CoRR abs/2006.05301 (2020) - [i15]Christopher K. I. Williams:
The Effect of Class Imbalance on Precision-Recall Curves. CoRR abs/2007.01905 (2020)
2010 – 2019
- 2019
- [c64]Charlie Nash, Nate Kushman, Christopher K. I. Williams:
Inverting Supervised Representations with Autoregressive Neural Density Models. AISTATS 2019: 1620-1629 - [c63]Alex Bird, Christopher K. I. Williams, Christopher Hawthorne:
Multi-Task Time Series Analysis applied to Drug Response Modelling. AISTATS 2019: 2174-2183 - [i14]Alex Bird, Christopher K. I. Williams, Christopher Hawthorne:
Multi-Task Time Series Analysis applied to Drug Response Modelling. CoRR abs/1903.08970 (2019) - [i13]Michael P. J. Camilleri
The Extended Dawid-Skene Model: Fusing Information from Multiple Data Schemas. CoRR abs/1906.01251 (2019) - [i12]Simão Eduardo, Alfredo Nazábal, Christopher K. I. Williams, Charles Sutton:
Robust Variational Autoencoders for Outlier Detection in Mixed-Type Data. CoRR abs/1907.06671 (2019) - [i11]Alex Bird, Christopher K. I. Williams:
Customizing Sequence Generation with Multi-Task Dynamical Systems. CoRR abs/1910.05026 (2019) - [i10]Taha Ceritli, Christopher K. I. Williams, James Geddes:
ptype: Probabilistic Type Inference. CoRR abs/1911.10081 (2019) - 2018
- [j34]Sohan Seth, Ahsan R. Akram, Kevin Dhaliwal
Estimating Bacterial and Cellular Load in FCFM Imaging. J. Imaging 4(1): 11 (2018) - [c62]Cian Eastwood, Christopher K. I. Williams:
A Framework for the Quantitative Evaluation of Disentangled Representations. ICLR (Poster) 2018 - [i9]Christopher K. I. Williams, Charlie Nash:
Autoencoders and Probabilistic Inference with Missing Data: An Exact Solution for The Factor Analysis Case. CoRR abs/1801.03851 (2018) - [i8]Charlie Nash, Nate Kushman, Christopher K. I. Williams:
Inverting Supervised Representations with Autoregressive Neural Density Models. CoRR abs/1806.00400 (2018) - [i7]Lukasz Romaszko, Christopher K. I. Williams, John M. Winn:
Learning Direct Optimization for Scene Understanding. CoRR abs/1812.07524 (2018) - 2017
- [j33]Charlie Nash, Christopher K. I. Williams:
The shape variational autoencoder: A deep generative model of part-segmented 3D objects. Comput. Graph. Forum 36(5): 1-12 (2017) - [c61]Lukasz Romaszko, Christopher K. I. Williams, Pol Moreno, Pushmeet Kohli:
Vision-as-Inverse-Graphics: Obtaining a Rich 3D Explanation of a Scene from a Single Image. ICCV Workshops 2017: 940-948 - [c60]Sohan Seth, Ahsan R. Akram, Kevin Dhaliwal
Estimating Bacterial Load in FCFM Imaging. MIUA 2017: 909-921 - 2016
- [c59]Pol Moreno, Christopher K. I. Williams, Charlie Nash, Pushmeet Kohli:
Overcoming Occlusion with Inverse Graphics. ECCV Workshops (3) 2016: 170-185 - [c58]Konstantinos Georgatzis, Christopher K. I. Williams, Christopher Hawthorne:
Input-Output Non-Linear Dynamical Systems applied to Physiological Condition Monitoring. MLHC 2016: 1-16 - [i6]Konstantinos Georgatzis, Christopher K. I. Williams, Christopher Hawthorne:
Input-Output Non-Linear Dynamical Systems applied to Physiological Condition Monitoring. CoRR abs/1608.00242 (2016) - [i5]Adam McCarthy, Christopher K. I. Williams:
Predicting Patient State-of-Health using Sliding Window and Recurrent Classifiers. CoRR abs/1612.00662 (2016) - 2015
- [j32]Mark Everingham, S. M. Ali Eslami, Luc Van Gool, Christopher K. I. Williams, John M. Winn, Andrew Zisserman:
The Pascal Visual Object Classes Challenge: A Retrospective. Int. J. Comput. Vis. 111(1): 98-136 (2015) - [c57]Konstantinos Georgatzis, Christopher K. I. Williams:
Discriminative Switching Linear Dynamical Systems applied to Physiological Condition Monitoring. UAI 2015: 306-315 - [i4]Konstantinos Georgatzis, Christopher K. I. Williams:
Discriminative Switching Linear Dynamical Systems applied to Physiological Condition Monitoring. CoRR abs/1504.06494 (2015) - [i3]Shell Xu Hu, Christopher K. I. Williams, Sinisa Todorovic:
Tree-Cut for Probabilistic Image Segmentation. CoRR abs/1506.03852 (2015) - 2014
- [j31]S. M. Ali Eslami, Nicolas Heess, Christopher K. I. Williams, John M. Winn:
The Shape Boltzmann Machine: A Strong Model of Object Shape. Int. J. Comput. Vis. 107(2): 155-176 (2014) - [j30]Ioan Stanculescu, Christopher K. I. Williams, Yvonne Freer:
Autoregressive Hidden Markov Models for the Early Detection of Neonatal Sepsis. IEEE J. Biomed. Health Informatics 18(5): 1560-1570 (2014) - [c56]Jyri J. Kivinen, Christopher K. I. Williams, Nicolas Heess:
Visual Boundary Prediction: A Deep Neural Prediction Network and Quality Dissection. AISTATS 2014: 512-521 - [c55]Ioan Stanculescu, Christopher K. I. Williams, Yvonne Freer:
A Hierarchical Switching Linear Dynamical System Applied to the Detection of Sepsis in Neonatal Condition Monitoring. UAI 2014: 752-761 - [i2]Amos J. Storkey, Nigel C. Hambly, Christopher K. I. Williams, Robert G. Mann:
Renewal Strings for Cleaning Astronomical Databases. CoRR abs/1408.1489 (2014) - 2013
- [b3]Robert B. Fisher, Toby P. Breckon, Kenneth M. Dawson-Howe, Andrew W. Fitzgibbon, Craig Robertson, Emanuele Trucco, Christopher K. I. Williams:
Dictionary of Computer Vision and Image Processing, Second Edition. Wiley 2013, ISBN 978-1-119-94186-6, pp. I-VIII, 1-372 - [j29]Krzysztof Chalupka, Christopher K. I. Williams, Iain Murray:
A framework for evaluating approximation methods for Gaussian process regression. J. Mach. Learn. Res. 14(1): 333-350 (2013) - 2012
- [j28]Andrew Zisserman, John M. Winn, Andrew W. Fitzgibbon, Luc Van Gool, Josef Sivic, Christopher K. I. Williams, David C. Hogg:
In Memoriam: Mark Everingham. IEEE Trans. Pattern Anal. Mach. Intell. 34(11): 2081-2082 (2012) - [c54]S. M. Ali Eslami, Christopher K. I. Williams:
A Generative Model for Parts-based Object Segmentation. NIPS 2012: 100-107 - [c53]Jyri J. Kivinen, Christopher K. I. Williams:
Multiple Texture Boltzmann Machines. AISTATS 2012: 638-646 - [i1]Krzysztof Chalupka, Christopher K. I. Williams, Iain Murray:
A Framework for Evaluating Approximation Methods for Gaussian Process Regression. CoRR abs/1205.6326 (2012) - 2011
- [j27]Bill Triggs, Christopher K. I. Williams:
Special Issue on Probabilistic Models for Image Understanding, Part II. Int. J. Comput. Vis. 95(3): 313-314 (2011) - [j26]Grigori Fursin, Yuriy Kashnikov, Abdul Wahid Memon, Zbigniew Chamski, Olivier Temam, Mircea Namolaru, Elad Yom-Tov
Milepost GCC: Machine Learning Enabled Self-tuning Compiler. Int. J. Parallel Program. 39(3): 296-327 (2011) - [j25]Stefan Harmeling, Christopher K. I. Williams:
Greedy Learning of Binary Latent Trees. IEEE Trans. Pattern Anal. Mach. Intell. 33(6): 1087-1097 (2011) - [c52]Christopher K. I. Williams, Ioan Stanculescu:
Automating the Calibration of a Neonatal Condition Monitoring System. AIME 2011: 240-249 - [c51]Seyed Mohammadali Eslami, Christopher K. I. Williams:
Factored Shapes and Appearances for Parts-based Object Understanding. BMVC 2011: 1-12 - [c50]Jyri J. Kivinen, Christopher K. I. Williams:
Transformation Equivariant Boltzmann Machines. ICANN (1) 2011: 1-9 - 2010
- [j24]Bill Triggs, Christopher K. I. Williams:
Editorial: Special Issue on Probabilistic Models for Image Understanding. Int. J. Comput. Vis. 88(2): 145-146 (2010) - [j23]Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John M. Winn, Andrew Zisserman:
The Pascal Visual Object Classes (VOC) Challenge. Int. J. Comput. Vis. 88(2): 303-338 (2010) - [e2]John D. Lafferty, Christopher K. I. Williams, John Shawe-Taylor, Richard S. Zemel, Aron Culotta:
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 [contents]
2000 – 2009
- 2009
- [j22]Moray Allan, Christopher K. I. Williams:
Object localisation using the Generative Template of Features. Comput. Vis. Image Underst. 113(7): 824-838 (2009) - [j21]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) - [c49]Nicolas Heess, Christopher K. I. Williams, Geoffrey E. Hinton:
Learning Generative Texture Models with extended Fields-of-Experts. BMVC 2009: 1-11 - [e1]Yoshua Bengio, Dale Schuurmans, John D. Lafferty, Christopher K. I. Williams, Aron Culotta:
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 [contents] - 2008
- [c48]John A. Quinn, Christopher K. I. Williams:
Signal masking in Gaussian channels. ICASSP 2008: 2989-2992 - [c47]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
- [c46]John A. Quinn, Christopher K. I. Williams:
Known Unknowns: Novelty Detection in Condition Monitoring. IbPRIA (1) 2007: 1-6 - [c45]Edwin V. Bonilla, Kian Ming Adam Chai, Christopher K. I. Williams:
Multi-task Gaussian Process Prediction. NIPS 2007: 153-160 - [c44]Edwin V. Bonilla, Felix V. Agakov, Christopher K. I. Williams:
Kernel Multi-task Learning using Task-specific Features. AISTATS 2007: 43-50 - 2006
- [b2]Carl Edward Rasmussen, Christopher K. I. Williams:
Gaussian processes for machine learning. Adaptive computation and machine learning, MIT Press 2006, ISBN 026218253X, pp. I-XVIII, 1-248 - [j20]Wolfgang P. Lehrach, Dirk Husmeier
A regularized discriminative model for the prediction of protein-peptide interactions. Bioinform. 22(5): 532-540 (2006) - [c43]Felix V. Agakov, Edwin V. Bonilla, John Cavazos, Björn Franke
Using Machine Learning to Focus Iterative Optimization. CGO 2006: 295-305 - [c42]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
Dataset Issues in Object Recognition. Toward Category-Level Object Recognition 2006: 29-48 - [c41]Michalis K. Titsias, Christopher K. I. Williams:
Sequential Learning of Layered Models from Video. Toward Category-Level Object Recognition 2006: 577-595 - [c40]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 - 2005
- [j19]Christopher K. I. Williams:
How to Pretend That Correlated Variables Are Independent by Using Difference Observations. Neural Comput. 17(1): 1-6 (2005) - [j18]John Shawe-Taylor
On the eigenspectrum of the gram matrix and the generalization error of kernel-PCA. IEEE Trans. Inf. Theory 51(7): 2510-2522 (2005) - [c39]Moray Allan, Michalis K. Titsias, Christopher K. I. Williams:
Fast Learning of Sprites using Invariant Features. BMVC 2005 - [c38]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
The 2005 PASCAL Visual Object Classes Challenge. MLCW 2005: 117-176 - [c37]Christopher K. I. Williams, John A. Quinn, Neil McIntosh:
Factorial Switching Kalman Filters for Condition Monitoring in Neonatal Intensive Care. NIPS 2005: 1513-1520 - [c36]Michalis K. Titsias, Christopher K. I. Williams:
Unsupervised Learning of Multiple Aspects of Moving Objects from Video. Panhellenic Conference on Informatics 2005: 746-756 - [c35]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 - 2004
- [j17]Christopher K. I. Williams, Michalis K. Titsias:
Greedy Learning of Multiple Objects in Images Using Robust Statistics and Factorial Learning. Neural Comput. 16(5): 1039-1062 (2004) - [c34]Michalis K. Titsias, Christopher K. I. Williams:
Fast Unsupervised Greedy Learning of Multiple Objects and Parts from Video. CVPR Workshops 2004: 179 - [c33]Peter Sollich, Christopher K. I. Williams:
Understanding Gaussian Process Regression Using the Equivalent Kernel. Deterministic and Statistical Methods in Machine Learning 2004: 211-228 - [c32]Moray Allan, Christopher K. I. Williams:
Harmonising Chorales by Probabilistic Inference. NIPS 2004: 25-32 - [c31]Peter Sollich, Christopher K. I. Williams:
Using the Equivalent Kernel to Understand Gaussian Process Regression. NIPS 2004: 1313-1320 - 2003
- [j16]Nicholas J. Adams, Christopher K. I. Williams:
Dynamic trees for image modelling. Image Vis. Comput. 21(10): 865-877 (2003) - [j15]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) - [c30]Matthias W. Seeger, Christopher K. I. Williams, Neil D. Lawrence:
Fast Forward Selection to Speed Up Sparse Gaussian Process Regression. AISTATS 2003: 254-261 - [c29]Max Welling, Felix V. Agakov, Christopher K. I. Williams:
Extreme Components Analysis. NIPS 2003: 137-144 - [c28]Miguel Á. Carreira-Perpiñán, Christopher K. I. Williams:
On the Number of Modes of a Gaussian Mixture. Scale-Space 2003: 625-640 - [c27]Amos J. Storkey, Nigel C. Hambly, Christopher K. I. Williams, Robert G. Mann:
Renewal Strings for Cleaning Astronomical Databases. UAI 2003: 559-566 - 2002
- [j14]Christopher K. I. Williams:
On a Connection between Kernel PCA and Metric Multidimensional Scaling. Mach. Learn. 46(1-3): 11-19 (2002) - [j13]Christopher K. I. Williams, Felix V. Agakov
Products of Gaussians and Probabilistic Minor Component Analysis. Neural Comput. 14(5): 1169-1182 (2002) - [j12]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) - [c26]John Shawe-Taylor
On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum. ALT 2002: 23-40 - [c25]John Shawe-Taylor
On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum. Discovery Science 2002: 12 - [c24]Nicholas J. Adams, Christopher K. I. Williams:
Dynamic Trees: Learning to Model Outdoor Scenes. ECCV (4) 2002: 82-96 - [c23]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 - [c22]Christopher K. I. Williams, Michalis K. Titsias:
Learning About Multiple Objects in Images: Factorial Learning without Factorial Search. NIPS 2002: 1391-1398 - 2001
- [j11]Francesco Vivarelli, Christopher K. I. Williams:
Comparing Bayesian neural network algorithms for classifying segmented outdoor images. Neural Networks 14(4-5): 427-437 (2001) - [c21]Amos J. Storkey, Christopher K. I. Williams:
Dynamic Positional Trees for Structural Image Analysis. AISTATS 2001: 286-292 - [c20]Christopher K. I. Williams, Felix V. Agakov, Stephen N. Felderhof:
Products of Gaussians. NIPS 2001: 1017-1024 - 2000
- [j10]Ian T. Nabney
Bayesian inference for wind field retrieval. Neurocomputing 30(1-4): 3-11 (2000) - [j9]Christopher K. I. Williams, Francesco Vivarelli:
Upper and Lower Bounds on the Learning Curve for Gaussian Processes. Mach. Learn. 40(1): 77-102 (2000) - [c19]Christopher K. I. Williams, Matthias W. Seeger:
The Effect of the Input Density Distribution on Kernel-based Classifiers. ICML 2000: 1159-1166 - [c18]Nicholas J. Adams, Amos J. Storkey, Christopher K. I. Williams, Zoubin Ghahramani:
MFDTs: Mean Field Dynamic Trees. ICPR 2000: 3151-3154 - [c17]Christopher K. I. Williams:
On a Connection between Kernel PCA and Metric Multidimensional Scaling. NIPS 2000: 675-681 - [c16]Christopher K. I. Williams, Matthias W. Seeger:
Using the Nyström Method to Speed Up Kernel Machines. NIPS 2000: 682-688
1990 – 1999
- 1999
- [c15]Christopher K. I. Williams:
A MCMC Approach to Hierarchical Mixture Modelling. NIPS 1999: 680-686 - 1998
- [j8]Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams:
Developments of the generative topographic mapping. Neurocomputing 21(1-3): 203-224 (1998) - [j7]Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams:
GTM: The Generative Topographic Mapping. Neural Comput. 10(1): 215-234 (1998) - [j6]Christopher K. I. Williams:
Computation with Infinite Neural Networks. Neural Comput. 10(5): 1203-1216 (1998) - [j5]Christopher K. I. Williams, David Barber:
Bayesian Classification With Gaussian Processes. IEEE Trans. Pattern Anal. Mach. Intell. 20(12): 1342-1351 (1998) - [c14]Giancarlo Ferrari-Trecate, Christopher K. I. Williams, Manfred Opper:
Finite-Dimensional Approximation of Gaussian Processes. NIPS 1998: 218-224 - [c13]Francesco Vivarelli, Christopher K. I. Williams:
Discovering Hidden Features with Gaussian Processes Regression. NIPS 1998: 613-619 - [c12]Christopher K. I. Williams, Nicholas J. Adams:
DTs: Dynamic Trees. NIPS 1998: 634-640 - [c11]Dan Cornford, Ian T. Nabney, Christopher K. I. Williams:
Adding Constrained Discontinuities to Gaussian Process Models of Wind Fields. NIPS 1998: 861-867 - [p1]Christopher K. I. Williams:
Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond. Learning in Graphical Models 1998: 599-621 - 1997
- [j4]Christopher K. I. Williams, Michael Revow, Geoffrey E. Hinton:
Instantiating Deformable Models with a Neural Net. Comput. Vis. Image Underst. 68(1): 120-126 (1997) - [c10]Paul W. Goldberg, Christopher K. I. Williams, Christopher M. Bishop:
Regression with Input-dependent Noise: A Gaussian Process Treatment. NIPS 1997: 493-499 - 1996
- [j3]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) - [c9]Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams:
GTM: A Principled Alternative to the Self-Organizing Map. ICANN 1996: 165-170 - [c8]Christopher K. I. Williams:
Computing with Infinite Networks. NIPS 1996: 295-301 - [c7]David Barber, Christopher K. I. Williams:
Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo. NIPS 1996: 340-346 - [c6]Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams:
GTM: A Principled Alternative to the Self-Organizing Map. NIPS 1996: 354-360 - 1995
- [j2]Richard S. Zemel, Christopher K. I. Williams, Michael Mozer:
Lending direction to neural networks. Neural Networks 8(4): 503-512 (1995) - [c5]Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams:
EM Optimization of Latent-Variables Density Models. NIPS 1995: 465-471 - [c4]Christopher K. I. Williams, Carl Edward Rasmussen:
Gaussian Processes for Regression. NIPS 1995: 514-520 - 1994
- [b1]Christopher K. I. Williams:
Combining deformable models and neural networks for handprinted digit recognition. University of Toronto, Canada, 1994 - [c3]Christopher K. I. Williams, Michael Revow, Geoffrey E. Hinton:
Using a neural net to instantiate a deformable model. NIPS 1994: 965-972 - 1992
- [j1]Michael C. Mozer, Richard S. Zemel, Marlene Behrmann, Christopher K. I. Williams:
Learning to Segment Images Using Dynamic Feature Binding. Neural Comput. 4(5): 650-665 (1992) - [c2]Richard S. Zemel, Christopher K. I. Williams, Michael Mozer:
Directional-Unit Boltzmann Machines. NIPS 1992: 172-179 - 1991
- [c1]Geoffrey E. Hinton, Christopher K. I. Williams, Michael Revow:
Adaptive Elastic Models for Hand-Printed Character Recognition. NIPS 1991: 512-519
Coauthor Index
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Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-12-23 20:33 CET by the dblp team
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