D. Mackay

发表

Speech dasher: fast writing using speech and gaze

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

Equivalence of Linear Boltzmann Chains and Hidden Markov Models

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

Failures of the One-Step Learning Algorithm

D. Mackay, D. MacKay, 2001 .

The Evidence Framework Applied to Classification Networks

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

Bayesian methods for adaptive models

D. Mackay, D. MacKay, 1992 .

Information Theory, Inference, and Learning Algorithms

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

Bayesian neural networks and density networks

D. Mackay, D. MacKay, 1995 .

Introduction to Gaussian processes

D. Mackay, D. MacKay, 1998 .

Bayesian Interpolation

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

Analysis of Linsker's application of Hebbian rules to linear networks

D. Mackay, K. Miller, 1990 .

Information-Based Objective Functions for Active Data Selection

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

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

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

Bayesian Model Comparison and Backprop Nets

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

Comparison of Approximate Methods for Handling Hyperparameters

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

Gaussian process modelling of austenite formation in steel

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

MCMC for Doubly-intractable Distributions

Zoubin Ghahramani, Iain Murray, David J. C. MacKay, 2006, UAI.

Bayesian Methods for Mixtures of Experts

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

Unsupervised Classifiers, Mutual Information and 'Phantom Targets'

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

The Role of Constraints in Hebbian Learning

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

Bayesian Methods for Backpropagation Networks

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

A hierarchical Dirichlet language model

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

HYPERPARAMETERS: OPTIMIZE, OR INTEGRATE OUT?

D. Mackay, D. MacKay, 1996 .

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..

Modelling creep rupture strength of ferritic steel welds

A. G. Sheard, D. Mackay, D. Cole, 2000 .

Choice of Basis for Laplace Approximation

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

Analysis of Linsker's Simulations of Hebbian Rules

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

Density networks

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

Introduction to Monte Carlo Methods

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

Elliptical slice sampling

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

Rate of Information Acquisition by a Species Subjected to Natural Selection

D. Mackay, 1999 .

Ensemble Learning for Blind Image Separation and Deconvolution

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

Bayesian Online Changepoint Detection

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

Independent Component Analysis: Ensemble Learning for blind source separation

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

BAYESIAN NON-LINEAR MODELING FOR THE PREDICTION COMPETITION

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

Fast and Flexible Selection with a Single Switch

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

A Revolution: Belief Propagation in Graphs with Cycles

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

Variational Gaussian process classifiers

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

Model for solidification cracking in low alloy steel weld metals

D. Mackay, H. Bhadeshia, K. Ichikawa, 1996 .

Bayesian Non-Linear Modelling with Neural Networks

David Mackay, D. Mackay, 1995 .

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 .

Neural network model of creep strength of austenitic stainless steels

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

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 .

A Practical Bayesian Framework for Backprop Networks

D. Mackay, D. MacKay, 1991 .

Ergodic pumping: A mechanism to drive biomolecular conformation changes

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

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

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

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

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

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 .

UNIVERSITY OF CAMBRIDGE PROGRAMME FOR INDUSTRY Neural Networks Summer School Bayesian Methods for Neural Networks: Theory and Applications

D. Mackay, 1995 .

Density Networks with Application to Protein Modelling Statement regarding Originality of Thesis

D. Mackay, T. Hubbard, 1998 .

Bayesian methods for supervised neural networks

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

Estimation of mechanical properties of ferritic steel welds. Part 1: Yield and tensile strength

David J. C. MacKay, H. K. D. H. Bhadeshia, D. Mackay, 2000 .

Design of New Creep-Resistant Nickel-Base Superalloys for Power-Plant Applications

David J. C. MacKay, Franck Tancret, H. K. D. H. Bhadeshia, 1999 .

Estimation of mechanical properties of ferritic steel welds. Part 2: Elongation and Charpy toughness

David J. C. MacKay, H. K. D. H. Bhadeshia, D. Mackay, 2000 .

Bayesian neural network model for austenite formation in steels

David J. C. MacKay, H. K. D. H. Bhadeshia, D. Mackay, 1996 .

MCMC for Doubly-intractable Distributions

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

A Practical Bayesian Framework for Backpropagation Networks

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