A Fast Procedure for Retraining the Multilayer Perceptron. - Christopher M. Bishop (Int. J. Neural Syst., 1991)

Reconstruction of Tokamak Density Profiles Using Feedforward Networks. - Christopher M. Bishop; Iain Strachan; John O'Rourke; Geoff Maddison; Paul Thomas (Neural Comput. Appl., 1993)

Curvature-driven smoothing: a learning algorithm for feedforward networks. - Christopher M. Bishop (IEEE Trans. Neural Networks, 1993)

Real-Time Control of a Tokamak Plasma Using Neural Networks. - Christopher M. Bishop (NIPS, 1994)

Estimating Conditional Probability Densities for Periodic Variables. - Christopher M. Bishop; Claire Legleye (NIPS, 1994)

Fast Feedback Control of a High Temperature Fusion Plasma. - Christopher M. Bishop; Paul S. Haynes; Mike E. U. Smith; Tom N. Todd; David L. Trotman (Neural Comput. Appl., 1994)

EM Optimization of Latent-Variables Density Models. - Christopher M. Bishop; Markus Svensén; Christopher K. I. Williams (NIPS, 1995)

Training with Noise is Equivalent to Tikhonov Regularization. - Christopher M. Bishop (Neural Comput., 1995)

Real-Time Control of a Tokamak Plasma Using Neural Networks. - Christopher M. Bishop; Paul S. Haynes; Mike E. U. Smith; Tom N. Todd; David L. Trotman (Neural Comput., 1995)

Bayesian Inference of Noise Levels in Regression. - Christopher M. Bishop; Cazhaow S. Quazaz (ICANN, 1996)

Bayesian Model Comparison by Monte Carlo Chaining. - David Barber; Christopher M. Bishop (NIPS, 1996)

Regression with Input-Dependent Noise: A Bayesian Treatment. - Christopher M. Bishop; Cazhaow S. Quazaz (NIPS, 1996)

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

Neural Networks. - Michael I. Jordan; Christopher M. Bishop (ACM Comput. Surv., 1996)

Modeling Conditional Probability Distributions for Periodic Variables. - Christopher M. Bishop; Ian T. Nabney (Neural Comput., 1996)

Neural Networks. - Michael I. Jordan; Christopher M. Bishop (The Computer Science and Engineering Handbook, 1997)

Ensemble Learning for Multi-Layer Networks. - David Barber; Christopher M. Bishop (NIPS, 1997)

Approximating Posterior Distributions in Belief Networks Using Mixtures. - Christopher M. Bishop; Neil D. Lawrence; Tommi S. Jaakkola; Michael I. Jordan (NIPS, 1997)

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

Bayesian Neural Networks. - Christopher M. Bishop (J. Braz. Comput. Soc., 1997)

Latent Variable Models. - Christopher M. Bishop (Learning in Graphical Models, 1998)

Bayesian PCA. - Christopher M. Bishop (NIPS, 1998)

Mixture Representations for Inference and Learning in Boltzmann Machines. - Neil D. Lawrence; Christopher M. Bishop; Michael I. Jordan (UAI, 1998)

Developments of the generative topographic mapping. - Christopher M. Bishop; Markus Svensén; Christopher K. I. Williams (Neurocomputing, 1998)

GTM: The Generative Topographic Mapping. - Christopher M. Bishop; Markus Svensén; Christopher K. I. Williams (Neural Comput., 1998)

A Hierarchical Latent Variable Model for Data Visualization. - Christopher M. Bishop; Michael E. Tipping (IEEE Trans. Pattern Anal. Mach. Intell., 1998)

Neural Network-Based Wind Vector Retrieval from Satellite Scatterometer Data. - Dan Cornford; Ian T. Nabney; Christopher M. Bishop (Neural Comput. Appl., 1999)

Mixtures of Probabilistic Principal Component Analysers. - Michael E. Tipping; Christopher M. Bishop (Neural Comput., 1999)

Non-linear Bayesian Image Modelling. - Christopher M. Bishop; John M. Winn (ECCV (1), 2000)

Variational Relevance Vector Machines. - Christopher M. Bishop; Michael E. Tipping (UAI, 2000)

Hyperparameters for Soft Bayesian Model Selection. - Adrian Corduneanu; Christopher M. Bishop (AISTATS, 2001)

Probabilistic Modelling of Replica Divergence. - Antony I. T. Rowstron; Neil D. Lawrence; Christopher M. Bishop (HotOS, 2001)

Optimising Synchronisation Times for Mobile Devices. - Neil D. Lawrence; Antony I. T. Rowstron; Christopher M. Bishop; M. J. Taylor (NIPS, 2001)

Feature representation and signal classification in fluorescence in-situ hybridization image analysis. - Boaz Lerner; William F. Clocksin; Seema Dhanjal; Maj A. Hultén; Christopher M. Bishop (IEEE Trans. Syst. Man Cybern. Part A, 2001)

VIBES: A Variational Inference Engine for Bayesian Networks. - Christopher M. Bishop; David J. Spiegelhalter; John M. Winn (NIPS, 2002)

Bayesian Image Super-Resolution. - Michael E. Tipping; Christopher M. Bishop (NIPS, 2002)

Super-resolution Enhancement of Video. - Christopher M. Bishop; Andrew Blake 001; Bhaskara Marthi (AISTATS, 2003)

Structured Variational Distributions in VIBES. - Christopher M. Bishop; John M. Winn (AISTATS, 2003)

Bayesian Hierarchical Mixtures of Experts. - Christopher M. Bishop; Markus Svensén (UAI, 2003)

Object Recognition via Local Patch Labelling. - Christopher M. Bishop; Ilkay Ulusoy (Deterministic and Statistical Methods in Machine Learning, 2004)

Distinguishing text from graphics in on-line handwritten ink. - Christopher M. Bishop; Markus Svensén; Geoffrey E. Hinton (IWFHR, 2004)

Generative models and Bayesian model comparison for shape recognition. - Balaji Krishnapuram; Christopher M. Bishop; Martin Szummer (IWFHR, 2004)

Generative versus Discriminative Methods for Object Recognition. - Ilkay Ulusoy; Christopher M. Bishop (CVPR (2), 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; et al. (MLCW, 2005)

Robust Bayesian mixture modelling. - Markus Svensén; Christopher M. Bishop (Neurocomputing, 2005)

Variational Message Passing. - John M. Winn; Christopher M. Bishop (J. Mach. Learn. Res., 2005)

Comparison of Generative and Discriminative Techniques for Object Detection and Classification. - Ilkay Ulusoy; Christopher M. Bishop (Toward Category-Level Object Recognition, 2006)

Principled Hybrids of Generative and Discriminative Models. - Julia A. Lasserre; Christopher M. Bishop; Thomas P. Minka (CVPR (1), 2006)

Pattern recognition and machine learning, 5th Edition. - Christopher M. Bishop (Springer, 2007)

Pattern Recognition and Machine Learning. - Christopher M. Bishop; Nasser M. Nasrabadi (J. Electronic Imaging, 2007)

A New Framework for Machine Learning. - Christopher M. Bishop (WCCI, 2008)

A unified modeling approach to data-intensive healthcare. - Iain E. Buchan; John M. Winn; Christopher M. Bishop (The Fourth Paradigm, 2009)

Embracing Uncertainty: The New Machine Intelligence. - Christopher M. Bishop (KES (1), 2010)

Embracing Uncertainty: Applied Machine Learning Comes of Age. - Christopher M. Bishop (ECML/PKDD (1), 2011)

Structural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables. - Nevena Lazic; Christopher M. Bishop; John M. Winn (AISTATS, 2013)

Neural Networks. - Michael I. Jordan; Christopher M. Bishop (Computing Handbook, 3rd ed. (1), 2014)

Students, Teachers, Exams and MOOCs: Predicting and Optimizing Attainment in Web-Based Education Using a Probabilistic Graphical Model. - Bar Shalem; Yoram Bachrach; John Guiver; Christopher M. Bishop (ECML/PKDD (3), 2014)

Deep Learning - Foundations and Concepts - Christopher M. Bishop; Hugh Bishop (Springer, 2024)

Accurate and scalable exchange-correlation with deep learning. - Giulia Luise; Chin-Wei Huang; Thijs Vogels; Derk P. Kooi; Sebastian Ehlert; Stephanie Lanius; Klaas J. H. Giesbertz; Amir Karton; Deniz Gunceler; Megan Stanley; Wessel P. Bruinsma; Lin Huang; et al. (CoRR, 2025)