David J. C. MacKay

发表

Dasher: A Gesture-Driven Data Entry Interface for Mobile Computing

Alan F. Blackwell, David J. Ward, David J. C. MacKay, 2002, Hum. Comput. Interact..

Low-density parity check codes over GF(q)

David J. C. MacKay, Matthew C. Davey, D. Mackay, 1998, IEEE Communications Letters.

The Gaussian Process Density Sampler

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

Neural Network Image Deconvolution

David J. C. MacKay, John E. Tansley, Martin J. Oldfield, 1996 .

The Statistical Model for Ticker, an Adaptive Single-Switch Text-Entry Method for Visually Impaired Users pdf

Per Ola Kristensson, David J. C. MacKay, Emli-Mari Nel, 2018, ArXiv.

MCMC for Doubly-intractable Distributions

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

Modelling Uncertainty in the Game of Go

Thore Graepel, David H. Stern, David J. C. MacKay, 2004, NIPS.

Model fitting as an aid to bridge balancing in neuronal recording

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

Density Networks and their Application to Protein Modelling

David J. C. MacKay, 1996 .

Qualitative reasoning: Modeling and simulation with incomplete knowledge

A. John Mallinckrodt, David J. C. MacKay, Benjamin Kuipers, 1994, at - Automatisierungstechnik.

Elliptical slice sampling

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

Good Error-Correcting Codes Based on Very Sparse Matrices

David J. C. MacKay, D. Mackay, 1997, IEEE Trans. Inf. Theory.

Information Theory, Inference, and Learning Algorithms

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

Hands-free Text Entry using Inverse Arithmetic Coding

David J. Ward, David J. C. MacKay, 2002 .

A decomposition model to track gene expression signatures: preview on observer-independent classification of ovarian cancer

David J. C. MacKay, Ann-Marie Martoglio, James W. Miskin, 2002, Bioinform..

Good error-correcting codes based on very sparse matrices

David J. C. MacKay, D. Mackay, 1997, Proceedings of IEEE International Symposium on Information Theory.

Static and Dynamic Modelling of Materials Forging

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

A Practical Bayesian Framework for Backpropagation Networks

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

Variational Gaussian process classifiers

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

Sparse low-density parity-check codes for channels with cross-talk

David J. C. MacKay, Edward A. Ratzer, 2003, Proceedings 2003 IEEE Information Theory Workshop (Cat. No.03EX674).

Almost-Certainly Runlength-Limiting Codes

David J. C. MacKay, D. Mackay, 2001, IMACC.

Dasher: A Gesture-Driven Data Entry Interface for Mobile Computing

Alan F. Blackwell, David J. C. MacKay, David J. Ward, 2002 .

Gallager Codes – Recent Results

David J. C. MacKay, D. Mackay, 2006 .

A hierarchical Dirichlet language model

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

Performance of low density parity check codes as a function of actual and assumed noise levels

David J. C. MacKay, Christopher P. Hesketh, D. Mackay, 2003, MFCSIT.

Gaussian Process Robust Regression for Noisy Heart Rate Data

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

The Nonnegative Boltzmann Machine

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

The Evidence Framework Applied to Classification Networks

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

Two Small Gallager Codes

David J. C. MacKay, Matthew C. Davey, 2001 .

Ensemble Learning for Blind Image Separation and Deconvolution

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

Good Codes Based on Very Sparse Matrices

David J. C. MacKay, D. Mackay, 1995, IMACC.

Equivalence of Linear Boltzmann Chains and Hidden Markov Models

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

Choice of Basis for Laplace Approximation

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

The Evidence for Neural Networks

David J. C. MacKay, 1992 .

Predicting and understanding the stability of G-quadruplexes

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

Comparison of constructions of irregular Gallager codes

David J. C. MacKay, Matthew C. Davey, Simon T. Wilson, 1999, IEEE Trans. Commun..

BSC Thresholds for Code Ensembles Based on “Typical Pairs” Decoding

Robert J. McEliece, David J. C. MacKay, Hui Jin, 2001 .

Developments in Probabilistic Modelling with Neural Networks - Ensemble Learning

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

Improving PPM with Dynamic Parameter Updates

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

Reliable communication over channels with insertions, deletions, and substitutions

David J. C. MacKay, Matthew C. Davey, D. Mackay, 2001, IEEE Trans. Inf. Theory.

Weaknesses of Margulis and Ramanujan-Margulis low-density parity-check cCodes

David J. C. MacKay, Michael S. Postol, D. Mackay, 2003, MFCSIT.

Interpolation models with multiple hyperparameters

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

Sparse-graph codes for quantum error correction

David J. C. MacKay, G. Mitchison, P. L. McFadden, 2004, IEEE Transactions on Information Theory.

Modelling Noise-Resilient Single-Switch Scanning Systems pdf

Per Ola Kristensson, David J. C. MacKay, Emli-Mari Nel, 2017, ArXiv.

Bayesian Comparison of Models for Images

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

Bayesian Interpolation

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

Fast Hands-free Writing by Gaze Direction

David J. Ward, David J. C. MacKay, David J. Ward, 2002, ArXiv.

A Revolution: Belief Propagation in Graphs with Cycles

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

Information-Based Objective Functions for Active Data Selection

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

Codes for Channels with Insertions, Deletions and Substitutions

David J. C. MacKay, Edward A. Ratzer, D. Mackay, 2000 .

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..

Reproducibility Assessment of Independent Component Analysis of Expression Ratios From DNA Microarrays

David J. C. MacKay, David Philip Kreil, 2003, Comparative and functional genomics.

Speech dasher: fast writing using speech and gaze

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

An Alternative to Runlength-limiting Codes: Turn Timing Errors into Substitution Errors

David J. C. MacKay, 2000 .

Unsupervised Classifiers, Mutual Information and 'Phantom Targets'

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

Efficient communication with one or two buttons

David J. C. MacKay, Mick Donegan, Chris J. Ball, 2004 .

The Role of Constraints in Hebbian Learning

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

A Free Energy Minimization Framework for Inference Problems in modulo 2 Arithmetic

David J. C. MacKay, D. Mackay, 1994, FSE.

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.

Encyclopedia of Sparse Graph Codes

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

BAYESIAN NON-LINEAR MODELING FOR THE PREDICTION COMPETITION

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

Eye tracking off the shelf

Mads Nielsen, John Paulin Hansen, Dan Witzner Hansen, 2004, ETRA.

Speech dasher: a demonstration of text input using speech and approximate pointing

Keith Vertanen, David J. C. MacKay, 2014, ASSETS.

Comparison of Approximate Methods for Handling Hyperparameters

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

Dasher—a data entry interface using continuous gestures and language models

Alan F. Blackwell, David J. Ward, David J. C. MacKay, 2000, UIST '00.

Implementation of Dasher, an information efficient input mechanism

Iain Murray, David J. Ward, David J. C. MacKay, 2003 .

Bayesian Methods for Backpropagation Networks

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

Bayesian methods for supervised neural networks

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

Nested sampling for Potts models

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

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 .

Bayesian neural network model for austenite formation in steels

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

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 .

Neural network model of creep strength of austenitic stainless steels

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

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 .

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 .

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 .

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

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