Sean B. Holden

发表

Learning Dynamic Systems from Time-series Data - An Application to Gene Regulatory Networks

Ivo J. P. M. Timóteo, Sean B. Holden, 2015, ICPRAM.

Bernard F. Buxton, Sean B. Holden, Jeevani Wickramaratna, 2001, Multiple Classifier Systems.

Generalization and learning in Volterra and radial basis function networks: a theoretical analysis

Sean B. Holden, Pjw Rayner, 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

Towards Graph Representation Learning in Emergent Communication pdf

Abhinav Gupta, Sean B. Holden, William L. Hamilton, 2020, ArXiv.

Removal of degeneracy in adaptive Volterra networks by dynamic structuring

Sean B. Holden, Pjw Rayner, M. R. Lynch, 1991, [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing.

PAC-like upper bounds for the sample complexity of leave-one-out cross-validation

Sean B. Holden, 1996, COLT '96.

Bayesian Optimisation of Solver Parameters in CBMC

Sean B. Holden, Lawrence C. Paulson, Chaitanya Mangla, 2020 .

The Generalized FITC Approximation

Sean B. Holden, Andrew Naish, S. Holden, 2007, NIPS.

STAR - Sparsity through Automated Rejection

Bernard F. Buxton, Sean B. Holden, Robert Burbidge, 2001, IWANN.

Complexity reduction in Volterra connectionist networks using a self-structuring LMS algorithm

Sean B. Holden, Pjw Rayner, M. R. Lynch, 1991 .

Support Vector Machines in Combinatorial Chemistry

Bernard F. Buxton, Sean B. Holden, Matthew Trotter, 2001 .

Quantifying Generalization in Linearly Weighted Neural Networks

Martin Anthony, Sean B. Holden, 1994, Complex Syst..

Drug Design by Machine Learning: Support Vector Machines for Pharmaceutical Data Analysis

Bernard F. Buxton, Sean B. Holden, Robert Burbidge, 2001, Comput. Chem..

On the Practical Applicability of VC Dimension Bounds

Mahesan Niranjan, Sean B. Holden, M. Niranjan, 1995, Neural Computation.

London WC1E6BT

Martin Anthony, Sean B. Holden, M. Anthony, 2007 .

On the theory of generalization and self-structuring in linearly weighted connectionist networks

Sean B. Holden, S. Holden, 1993 .

Proceedings of the Automated Reasoning Workshop 2008

Abdur Rakib, Michael Fisher, Alexander Bolotov, 2008 .

Bayesian Optimisation for Premise Selection in Automated Theorem Proving (Student Abstract)

Sean B. Holden, Mateja Jamnik, Agnieszka Slowik, 2020, AAAI.

Machine Learning for First-Order Theorem Proving

Lawrence C. Paulson, Sean B. Holden, James P. Bridge, 2014, Journal of Automated Reasoning.

HasGP: A Haskell library for Gaussian process inference

Sean B. Holden, 2016 .

Bayesian Optimisation with Gaussian Processes for Premise Selection pdf

Lawrence C. Paulson, Sean B. Holden, Mateja Jamnik, 2019, ArXiv.

Learning Dynamic Systems from Time-series Data

Sean B. Holden, Ivo J. P. M. Timoteo, 2015, ICPRAM 2015.

On the statistical physics of radial basis function networks

Mahesan Niranjan, Sean B. Holden, 2005, Neural Processing Letters.

Exploring Structural Inductive Biases in Emergent Communication pdf

Abhinav Gupta, Sean B. Holden, William L. Hamilton, 2020, ArXiv.

Accurate parallel form filter synthesis

Mark Sandler, Sean B. Holden, Marc Price, 1996 .

Bayesian Optimisation for Heuristic Configuration in Automated Theorem Proving

Sean B. Holden, Mateja Jamnik, Agnieszka Slowik, 2020, Vampire Workshop.

Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics

Kathryn S. Lilley, Oliver Kohlbacher, Sean B. Holden, 2015, bioRxiv.

Support Vector Machines for ADME Property Classification

Sean B. Holden, Matthew Trotter, 2003 .

On the Explicit Use of Example Weights in the Construction of Classifiers

Ulrich Paquet, Sean B. Holden, Andrew Naish, 2005, ICANN.

Generalization and PAC learning: some new results for the class of generalized single-layer networks

Peter J. W. Rayner, Sean B. Holden, S. Holden, 1995, IEEE Trans. Neural Networks.

Robust Regression with Twinned Gaussian Processes

Sean B. Holden, Andrew Naish, S. Holden, 2007, NIPS.

Bayesian Hierarchical Ordinal Regression

Ulrich Paquet, Sean B. Holden, Andrew Naish, 2005, ICANN.

Co-complex protein membership evaluation using Maximum Entropy on GO ontology and InterPro annotation

Kathryn S. Lilley, Sean B. Holden, Matthew W. B. Trotter, 2018, Bioinform..

Survey propagation applied to weighted partial maximum satisfiability

Sean B. Holden, Richard Russell, 2016 .

The HasGP user manual

Sean B. Holden, 2011 .

On the power of polynomial discriminators and radial basis function networks

Martin Anthony, Sean B. Holden, M. Anthony, 1993, COLT '93.

Average-Case Learning Curves for Radial Basis Function Networks

Mahesan Niranjan, Sean B. Holden, M. Niranjan, 1997, Neural Computation.

Machine Learning for Automated Theorem Proving: Learning to Solve SAT and QSAT

Sean B. Holden, S. Holden, 2021, Found. Trends Mach. Learn..

On The Power of Linearly Weighted Neural Networks

Martin Anthony, Sean B. Holden, M. Anthony, 1993 .