D. MacKay

发表

Ensemble Learning for Blind Source Separation

J. W. Miskin, D.J.C. MacKay, D. Mackay, 2001 .

Information Theory, Inference, and Learning Algorithms

David J. C. MacKay, D. Mackay, D. MacKay, 2004, IEEE Transactions on Information Theory.

HYPERPARAMETERS: OPTIMIZE, OR INTEGRATE OUT?

D. Mackay, D. MacKay, 1996 .

Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks

David Mackay, D. Mackay, D. MacKay, 1995 .

Gaussian process modelling of austenite formation in steel

D. Mackay, C. Bailer-Jones, H. Bhadeshia, 1999 .

Bayesian Interpolation

David J. C. MacKay, D. Mackay, D. MacKay, 1992, Neural Computation.

Estimation of the amount of retained austenite in austempered ductile irons using neural networks

H. K. D. H. Bhadeshia, D.J.C. MacKay, D. Mackay, 2001 .

Information-Based Objective Functions for Active Data Selection

David J. C. MacKay, D. Mackay, D. MacKay, 1992, Neural Computation.

Estimation of the γ and γ' lattice parameters in nickel-base superalloys using neural network analysis

H. K. D. H. Bhadeshia, D. J. C. Mackay, S. Yoshitake, 1998 .

Bayesian Methods for Backpropagation Networks

David J. C. MacKay, D. Mackay, D. MacKay, 1996 .

Elliptical slice sampling

Ryan P. Adams, Iain Murray, David J. C. MacKay, 2009, AISTATS.

Comparison of Approximate Methods for Handling Hyperparameters

David J. C. MacKay, D. Mackay, D. MacKay, 1999, Neural Computation.

Predicting and understanding the stability of G-quadruplexes

Oliver Stegle, David J. C. MacKay, Jean-Louis Mergny, 2009, Bioinform..

Turbo Decoding as an Instance of Pearl's "Belief Propagation" Algorithm

Jung-Fu Cheng, Robert J. McEliece, David J. C. MacKay, 1998, IEEE J. Sel. Areas Commun..

A hierarchical Dirichlet language model

David J. C. MacKay, Linda C. Bauman Peto, D. Mackay, 1995, Natural Language Engineering.

The Evidence Framework Applied to Classification Networks

David J. C. MacKay, D. Mackay, D. MacKay, 1992, Neural Computation.

Analysis of Linsker's Simulations of Hebbian Rules

David J. C. MacKay, Kenneth D. Miller, D. Mackay, 1990, Neural Computation.

Bayesian Comparison of Models for Images

David J. C. MacKay, Alex H. Barnett, A. Barnett, 1996 .

Bayesian Methods for Mixtures of Experts

Steve R. Waterhouse, Anthony J. Robinson, A. J. Robinson, 1995, NIPS.

A Practical Bayesian Framework for Backpropagation Networks

David J. C. MacKay, D. Mackay, D. MacKay, 1992, Neural Computation.

The Role of Constraints in Hebbian Learning

Kenneth D. Miller, David J. C. MacKay, D. Mackay, 1994, Neural Computation.

Nonparametric Bayesian Density Modeling with Gaussian Processes

Ryan P. Adams, Iain Murray, D. MacKay, 2009, 0912.4896.

Nested sampling for Potts models

Zoubin Ghahramani, Iain Murray, John Skilling, 2005, NIPS.

Ergodic pumping: A mechanism to drive biomolecular conformation changes

D. Mackay, R. MacKay, D. MacKay, 2006 .

MCMC for Doubly-intractable Distributions

Iain Murray, D. Mackay, Zoubin Ghahramani, 2006, UAI.

Introduction to Gaussian processes

D. Mackay, D. MacKay, 1998 .

BAYESIAN NON-LINEAR MODELING FOR THE PREDICTION COMPETITION

David J. C. MacKay, D. Mackay, D. MacKay, 1996 .

Bayesian methods for adaptive models

D. Mackay, D. MacKay, 1992 .

Unsupervised Classifiers, Mutual Information and 'Phantom Targets'

David J. C. MacKay, John S. Bridle, Anthony J. R. Heading, 1991, NIPS.

Choice of Basis for Laplace Approximation

David J. C. MacKay, D. Mackay, D. MacKay, 1998, Machine Learning.

The Gaussian Process Density Sampler

Ryan P. Adams, Iain Murray, David J. C. MacKay, 2008, NIPS.

Gaussian Processes - A Replacement for Supervised Neural Networks?

David Mackay, D. MacKay, 1997 .

A Revolution: Belief Propagation in Graphs with Cycles

Brendan J. Frey, David J. C. MacKay, B. Frey, 1997, NIPS.

Fast and Flexible Selection with a Single Switch

Tamara Broderick, David John Cameron MacKay, D. Mackay, 2009, PloS one.

A Practical Bayesian Framework for Backprop Networks

D. Mackay, D. MacKay, 1991 .

Bayesian Neural Network Analysis of Fatigue Crack Growth Rate in Nickel Base Superalloys

D. Mackay, H. Bhadeshia, H. Fujii, 1996 .

Bayesian Online Changepoint Detection

Ryan P. Adams, David J.C. MacKay, Ryan Prescott Adams, 2007, 0710.3742.

Gaussian Process Robust Regression for Noisy Heart Rate Data

Oliver Stegle, Søren Brage, David J. C. MacKay, 2008, IEEE Transactions on Biomedical Engineering.

Introduction to Monte Carlo Methods

David J. C. Mackay, D. Mackay, D. MacKay, 1998, Learning in Graphical Models.

Neural network model of creep strength of austenitic stainless steels

David J. C. MacKay, Thomas Sourmail, H. K. D. H. Bhadeshia, 2002 .

Bayesian Analysis of Linear Phased-Array Radar

A. Green, D. MacKay, 1996 .

Equivalence of Linear Boltzmann Chains and Hidden Markov Models

David J. C. MacKay, D. Mackay, D. MacKay, 1996, Neural Computation.

Impact toughness of C-Mn steel arc welds : Bayesian neural network analysis

D. Mackay, L. Svensson, H. Bhadeshia, 1995 .

A recurrent neural network for modelling dynamical systems.

C A Bailer-Jones, D J MacKay, P J Withers, 1998, Network.

Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities

Ryan P. Adams, Iain Murray, David J. C. MacKay, 2009, ICML '09.

Bayesian Model Comparison and Backprop Nets

David J. C. MacKay, D. Mackay, D. MacKay, 1991, NIPS.

Theoretical design of ferritic creep resistant steels using neural network, kinetic, and thermodynamic models

David J. C. MacKay, H. K. D. H. Bhadeshia, Joseph D. Robson, 1999 .

Design of a creep resistant nickel base superalloy for power plant applications: Part 1 - Mechanical properties modelling

David J. C. MacKay, Franck Tancret, H. K. D. H. Bhadeshia, 2003 .

Variational Gaussian process classifiers

David J. C. MacKay, Mark N. Gibbs, D. Mackay, 2000, IEEE Trans. Neural Networks Learn. Syst..

Tensile properties of mechanically alloyed oxide dispersion strengthened iron alloys Part 1 - Neural networkmodels

H. K. D. H. Bhadeshia, D. J. C. Mackay, D. Mackay, 1998 .

Static and Dynamic Modelling of Materials Forging

Philip J. Withers, Coryn A. L. Bailer-Jones, David J. C. MacKay, 2001 .

Ensemble Learning for Blind Image Separation and Deconvolution

David J. C. MacKay, James W. Miskin, D. Mackay, 2000 .

Speech dasher: fast writing using speech and gaze

Keith Vertanen, David J. C. MacKay, K. Vertanen, 2010, CHI.

Bayesian neural networks and density networks

D. Mackay, D. MacKay, 1995 .

Density networks

David J.C. Mackay, Mark N. Gibbs, D. Mackay, 2000 .

PREDICTION OF DEFORMED AND ANNEALED MICROSTRUCTURES USING BAYESIAN NEURAL NETWORKS AND GAUSSIAN PROCESSES

Coryn A. L. Bailer-Jones, David J. C. MacKay, Paul J. A. Withers, 1997 .

Failures of the One-Step Learning Algorithm

D. Mackay, D. MacKay, 2001 .

Improving PPM with Dynamic Parameter Updates

Zoubin Ghahramani, David J. C. MacKay, Christian Steinruecken, 2015, 2015 Data Compression Conference.

Consistency of The Mortality of Chronically-irradiated Beagles with the Linear No-Threshold Model

D. MacKay, 2014 .

Model fitting as an aid to bridge balancing in neuronal recording

Virginia R. de Sa, David J. C. MacKay, V. D. Sa, 2001, Neurocomputing.

Predicting and understanding the stability of G-quadruplexes

J. Huppert, O. Stegle, J. Mergny, 2009, Bioinform..

A Revolution: Belief Propagation Graphs with Cycles

B. Frey, D. MacKay, 1998 .

Introduction to Monte Carlo Methods

D. MacKay, 1998, Learning in Graphical Models.

The Nonnegative Boltzmann Machine

Daniel D. Lee, David J. C. MacKay, Oliver B. Downs, 1999, NIPS.

Interpolation models with multiple hyperparameters

Ryo Takeuchi, David J. C. MacKay, D. MacKay, 1998, Stat. Comput..

Developments in Probabilistic Modelling with Neural Networks - Ensemble Learning

David J. C. MacKay, D. MacKay, 1995, SNN Symposium on Neural Networks.