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ORCIDChristopher M. Bishop
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- affiliation: Microsoft Research
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2020 – today
- 2024
[b2]Christopher M. Bishop, Hugh Bishop:
Deep Learning - Foundations and Concepts. Springer 2024, ISBN 978-3-031-45467-7, pp. 1-607
2010 – 2019
- 2014
- [c36]Bar Shalem, Yoram Bachrach, John Guiver, Christopher M. Bishop:
Students, Teachers, Exams and MOOCs: Predicting and Optimizing Attainment in Web-Based Education Using a Probabilistic Graphical Model. ECML/PKDD (3) 2014: 82-97 - [p3]Michael I. Jordan, Christopher M. Bishop:
Neural Networks. Computing Handbook, 3rd ed. (1) 2014: 42: 1-24 - 2013
- [c35]Nevena Lazic, Christopher M. Bishop, John M. Winn:
Structural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables. AISTATS 2013: 379-387 - [i3]Christopher M. Bishop, Michael E. Tipping:
Variational Relevance Vector Machines. CoRR abs/1301.3838 (2013) - [i2]Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan:
Mixture Representations for Inference and Learning in Boltzmann Machines. CoRR abs/1301.7393 (2013) - 2012
- [i1]Christopher M. Bishop, Markus Svensén:
Bayesian Hierarchical Mixtures of Experts. CoRR abs/1212.2447 (2012) - 2011
- [c34]Christopher M. Bishop:
Embracing Uncertainty: Applied Machine Learning Comes of Age. ECML/PKDD (1) 2011: 4 - 2010
- [c33]Christopher M. Bishop:
Embracing Uncertainty: The New Machine Intelligence. KES (1) 2010: 3
2000 – 2009
- 2009
- [p2]Iain E. Buchan, John M. Winn, Christopher M. Bishop:
A unified modeling approach to data-intensive healthcare. The Fourth Paradigm 2009: 91-97 - 2008
- [c32]Christopher M. Bishop:
A New Framework for Machine Learning. WCCI 2008: 1-24 - 2007
- [b1]Christopher M. Bishop:
Pattern recognition and machine learning, 5th Edition. Information science and statistics, Springer 2007, ISBN 9780387310732, pp. I-XX, 1-738 - [j18]Christopher M. Bishop, Nasser M. Nasrabadi:
Pattern Recognition and Machine Learning. J. Electronic Imaging 16(4): 049901 (2007) - 2006
- [c31]Ilkay Ulusoy, Christopher M. Bishop:
Comparison of Generative and Discriminative Techniques for Object Detection and Classification. Toward Category-Level Object Recognition 2006: 173-195 - [c30]Julia A. Lasserre, Christopher M. Bishop, Thomas P. Minka:
Principled Hybrids of Generative and Discriminative Models. CVPR (1) 2006: 87-94 - 2005
- [j17]Markus Svensén, Christopher M. Bishop:
Robust Bayesian mixture modelling. Neurocomputing 64: 235-252 (2005) - [j16]John M. Winn, Christopher M. Bishop:
Variational Message Passing. J. Mach. Learn. Res. 6: 661-694 (2005) - [c29]Ilkay Ulusoy, Christopher M. Bishop:
Generative versus Discriminative Methods for Object Recognition. CVPR (2) 2005: 258-265 - [c28]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:
The 2005 PASCAL Visual Object Classes Challenge. MLCW 2005: 117-176 - 2004
- [c27]Christopher M. Bishop, Ilkay Ulusoy:
Object Recognition via Local Patch Labelling. Deterministic and Statistical Methods in Machine Learning 2004: 1-21 - [c26]Christopher M. Bishop, Markus Svensén:
Robust Bayesian Mixture Modelling. ESANN 2004: 69-74 - [c25]Balaji Krishnapuram, Christopher M. Bishop, Martin Szummer:
Generative models and Bayesian model comparison for shape recognition. IWFHR 2004: 20-25 - [c24]Christopher M. Bishop, Markus Svensén, Geoffrey E. Hinton:
Distinguishing text from graphics in on-line handwritten ink. IWFHR 2004: 142-147 - 2003
- [c23]Christopher M. Bishop, Andrew Blake, Bhaskara Marthi:
Super-resolution Enhancement of Video. AISTATS 2003: 25-32 - [c22]Christopher M. Bishop, John M. Winn:
Structured Variational Distributions in VIBES. AISTATS 2003: 33-40 - [c21]Christopher M. Bishop, Markus Svensén:
Bayesian Hierarchical Mixtures of Experts. UAI 2003: 57-64 - [e1]Christopher M. Bishop, Brendan J. Frey:
Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, AISTATS 2003, Key West, Florida, USA, January 3-6, 2003. Society for Artificial Intelligence and Statistics 2003, ISBN 0-9727358-0-1 [contents] - 2002
- [c20]Christopher M. Bishop, David J. Spiegelhalter, John M. Winn:
VIBES: A Variational Inference Engine for Bayesian Networks. NIPS 2002: 777-784 - [c19]Michael E. Tipping, Christopher M. Bishop:
Bayesian Image Super-Resolution. NIPS 2002: 1279-1286 - 2001
- [j15]Boaz Lerner, William F. Clocksin, Seema Dhanjal, Maj A. Hultén, Christopher M. Bishop:
Feature representation and signal classification in fluorescence in-situ hybridization image analysis. IEEE Trans. Syst. Man Cybern. Part A 31(6): 655-665 (2001) - [c18]Adrian Corduneanu, Christopher M. Bishop:
Hyperparameters for Soft Bayesian Model Selection. AISTATS 2001: 63-70 - [c17]Antony I. T. Rowstron, Neil D. Lawrence, Christopher M. Bishop:
Probabilistic Modelling of Replica Divergence. HotOS 2001: 55-60 - [c16]Neil D. Lawrence, Antony I. T. Rowstron, Christopher M. Bishop, M. J. Taylor:
Optimising Synchronisation Times for Mobile Devices. NIPS 2001: 1401-1408 - 2000
- [c15]Christopher M. Bishop, John M. Winn:
Non-linear Bayesian Image Modelling. ECCV (1) 2000: 3-17 - [c14]Christopher M. Bishop, Michael E. Tipping:
Variational Relevance Vector Machines. UAI 2000: 46-53
1990 – 1999
- 1999
- [j14]Dan Cornford, Ian T. Nabney, Christopher M. Bishop:
Neural Network-Based Wind Vector Retrieval from Satellite Scatterometer Data. Neural Comput. Appl. 8(3): 206-217 (1999) - [j13]Michael E. Tipping, Christopher M. Bishop:
Mixtures of Probabilistic Principal Component Analysers. Neural Comput. 11(2): 443-482 (1999) - 1998
- [j12]Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams:
Developments of the generative topographic mapping. Neurocomputing 21(1-3): 203-224 (1998) - [j11]Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams:
GTM: The Generative Topographic Mapping. Neural Comput. 10(1): 215-234 (1998) - [j10]Christopher M. Bishop, Michael E. Tipping:
A Hierarchical Latent Variable Model for Data Visualization. IEEE Trans. Pattern Anal. Mach. Intell. 20(3): 281-293 (1998) - [c13]Christopher M. Bishop:
Bayesian PCA. NIPS 1998: 382-388 - [c12]Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan:
Mixture Representations for Inference and Learning in Boltzmann Machines. UAI 1998: 320-327 - [p1]Christopher M. Bishop:
Latent Variable Models. Learning in Graphical Models 1998: 371-403 - 1997
- [j9]Christopher M. Bishop:
Bayesian Neural Networks. J. Braz. Comput. Soc. 4(1) (1997) - [c11]David Barber, Christopher M. Bishop:
Ensemble Learning for Multi-Layer Networks. NIPS 1997: 395-401 - [c10]Christopher M. Bishop, Neil D. Lawrence, Tommi S. Jaakkola, Michael I. Jordan:
Approximating Posterior Distributions in Belief Networks Using Mixtures. NIPS 1997: 416-422 - [c9]Paul W. Goldberg, Christopher K. I. Williams, Christopher M. Bishop:
Regression with Input-dependent Noise: A Gaussian Process Treatment. NIPS 1997: 493-499 - [r1]Michael I. Jordan, Christopher M. Bishop:
Neural Networks. The Computer Science and Engineering Handbook 1997: 536-556 - 1996
- [j8]Michael I. Jordan, Christopher M. Bishop:
Neural Networks. ACM Comput. Surv. 28(1): 73-75 (1996) - [j7]Christopher M. Bishop, Ian T. Nabney:
Modeling Conditional Probability Distributions for Periodic Variables. Neural Comput. 8(5): 1123-1133 (1996) - [c8]Christopher M. Bishop, Cazhaow S. Quazaz:
Bayesian Inference of Noise Levels in Regression. ICANN 1996: 59-64 - [c7]Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams:
GTM: A Principled Alternative to the Self-Organizing Map. ICANN 1996: 165-170 - [c6]David Barber, Christopher M. Bishop:
Bayesian Model Comparison by Monte Carlo Chaining. NIPS 1996: 333-339 - [c5]Christopher M. Bishop, Cazhaow S. Quazaz:
Regression with Input-Dependent Noise: A Bayesian Treatment. NIPS 1996: 347-353 - [c4]Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams:
GTM: A Principled Alternative to the Self-Organizing Map. NIPS 1996: 354-360 - 1995
- [j6]Christopher M. Bishop:
Training with Noise is Equivalent to Tikhonov Regularization. Neural Comput. 7(1): 108-116 (1995) - [j5]Christopher M. Bishop, Paul S. Haynes, Mike E. U. Smith, Tom N. Todd, David L. Trotman:
Real-Time Control of a Tokamak Plasma Using Neural Networks. Neural Comput. 7(1): 206-217 (1995) - [c3]Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams:
EM Optimization of Latent-Variables Density Models. NIPS 1995: 465-471 - 1994
- [j4]Christopher M. Bishop, Paul S. Haynes, Mike E. U. Smith, Tom N. Todd, David L. Trotman:
Fast Feedback Control of a High Temperature Fusion Plasma. Neural Comput. Appl. 2(3): 148-159 (1994) - [c2]Christopher M. Bishop, Claire Legleye:
Estimating Conditional Probability Densities for Periodic Variables. NIPS 1994: 641-648 - [c1]Christopher M. Bishop:
Real-Time Control of a Tokamak Plasma Using Neural Networks. NIPS 1994: 1007-1014 - 1993
- [j3]Christopher M. Bishop, Iain Strachan, John O'Rourke, Geoff Maddison, Paul Thomas:
Reconstruction of Tokamak Density Profiles Using Feedforward Networks. Neural Comput. Appl. 1(1): 4-16 (1993) - [j2]Christopher M. Bishop:
Curvature-driven smoothing: a learning algorithm for feedforward networks. IEEE Trans. Neural Networks 4(5): 882-884 (1993) - 1991
- [j1]Christopher M. Bishop:
A Fast Procedure for Retraining the Multilayer Perceptron. Int. J. Neural Syst. 2(3): 229-236 (1991)
Coauthor Index
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last updated on 2024-10-07 21:22 CEST by the dblp team
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