E. V. Bonilla, Felix V. Agakov, C. K. I. Williams.
J. Cavazos, C. Dubach, F. V. Agakov, E. V. Bonilla, M. F. P. O'Boyle, G.
Fursin, O. Temam.
E. V. Bonilla, C. K. I. Williams, F. V. Agakov, J. Cavazos, J. Thomson,
M. F. P. O'Boyle.
F. V. Agakov, E. V. Bonilla, J. Cavazos, B. Franke, G. Fusin, M. F. P.
O'Boyle, J. Thomson, M. Toussaint, C. K. I. Williams.
F. V. Agakov and D. Barber. Auxiliary Variational Information
Maximization for Dimensionality Reduction.
F.V. Agakov and D. Barber. Kernelized Infomax Clustering.
F. V. Agakov and D. Barber. Variational Information Maximization for
Neural Coding.
In International Conference on Neural Information Processing,
Springer, 2004, 6p. [pdf]
F. V. Agakov and D. Barber. An Auxiliary Variational Method.
D. Barber and F. V. Agakov. The IM Algorithm: A variational
approach to Information Maximization.
M. Welling, F. V. Agakov and C. K. I. Williams. Extreme Components
Analysis.
F. V. Agakov and D. Barber. Approximate Learning in Temporal Hidden
Hopfield Models.
C. K. I. Williams and F. V. Agakov.
Products of Gaussians and Probabilistic Minor Component
Analysis.
C. K. I. Williams, F. V. Agakov and S. N. Felderhof.
Products of Gaussians.
Kernel Multi-task Learning using Task-specific Features.
To appear in AISTATS 2007, The Society for Artificial Intelligence and
Statistics, 8p. [ pdf]
Automatic Performance Model Construction for the
Fast Software Exploration of New Hardware Designs.
In Proc. Int. Conf. on Compilers, Architecture, and Synthesis
for Embedded Systems (CASES), ACM, 2006, 11p.
[ pdf]
Predictive Search Distributions.
In Proc. ICML, OmniPress, 2006, 8p. [pdf]
Using Machine Learning to Focus Iterative Optimization.
In the 4th Annual International Symposium on Code Generation and
Optimization (CGO), IEEE Comp. Soc., 2006, 11p.
[pdf]
In C. Saunders, M. Grobelnik, S. Gunn, J. Shawe-Taylor (Eds.),
Revised selected papers of Subspace, Latent Structure, and Feature
Selection Workshop (SLSFS), LNCS 3940, Springer,
2006, 12p. [ps.gz,
pdf]
In Neural Information Processing Systems 18, MIT Press, 2005, 8p.
[ps.gz,
pdf]
In International Conference on Neural Information Processing,
Springer, 2004, 6p. [pdf]
In Neural Information Processing Systems 16, MIT Press, 2003, 8p. [ps.gz,
pdf]
In Neural Information Processing Systems 16, MIT Press, 2003, 8p. [ps.gz, pdf]
In International Conference on Artificial Neural Networks, Springer-Verlag, 2003,
8p. [ps.gz,
pdf].
Neural Computation, Vol. 14, No. 5, MIT Press, 2002.
[ps.gz]
In Neural Information Processing Systems 14, MIT Press, 2001, 8p.
[ps.gz]
F. V. Agakov and D. Barber. Variational Information Maximization in
Gaussian Channels.
F. V. Agakov and D. Barber. An Auxiliary Variational Method.
F. V. Agakov and D. Barber. Temporal Hidden Hopfield Models.
D. Barber and F. V. Agakov. Correlated sequence learning in a network
C. K. I. Williams and F. V. Agakov.
An Analysis of Contrastive Divergence
C. K. I. Williams and F. V. Agakov.
Products of Gaussians and Probabilistic Minor Component
Analysis.
UoE, IANC. Technical Report, EDI-INF-RR-0206, Apr 2004, 12p.
[ps.gz, pdf]
UoE, IANC. Technical Report, EDI-INF-RR-0205, Apr 2004, 10p.
[ps.gz, pdf]
UoE, IANC. Technical Report, EDI-INF-RR-0156, Nov 2002, 18p. [ps.gz,
pdf]
(Here are the slides for the related talk
I gave at the Probabilistic Brain symposium in Cambridge,
UK '03).
of spiking neurons using maximum likelihood.
UoE, IANC. Technical Report, EDI-INF-RR-0149, Apr 2002, 13p.
[ps.gz]
Learning in Gaussian
Boltzmann Machines.
UoE, IANC. Technical Report, EDI-INF-RR-0120, May 2002, 14p.
[ps.gz]
UoE, Institute of Adaptive and Neural Computation.
Technical Report, EDI-INF-RR-0043, July 2001, 11p.
[ps.gz]
F. V. Agakov and D. Barber. Information-Theoretic Clustering
in Nonlinear Encoder Models.
F. V. Agakov and D. Barber. Auxiliary Variational Information
Maximization for Dimensionality Reduction.
In PASCAL: Statistics and
Optimization of Clustering Workshop, 2005.
[pdf]
In PASCAL: Subspace, Latent Structure and Feature Selection Techniques:
Statistical and Optimisation Perspectives Workshop, 2005.
[ps.gz,
pdf]
F. V. Agakov. Variational Information Maximization in
Stochastic Environments.
F. V. Agakov. Investigations of Gaussian Products-of-Experts
Models.
Here is the list of older
publications.
PhD Thesis, School of Informatics, The University of Edinburgh,
2005, 205p. [pdf]
Master's Thesis, Division of Informatics, The University of Edinburgh,
2000, 169p. [ps.gz]
(See also introductory [ps.gz]
and even more introductory [ps.gz]
slides).
Here are updates of supporting calculations for the variance of the
weight update in contrastive divergence learning in a simple 1-factor
1-D factor analysis model [ps.gz].
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Page last updated: Feb. 11, 2007