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John Shawe-Taylor

Showing results (1-10 of 39) with videos related to

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Scientific Reports|September 29, 2022
Artificial intelligence-informed planning for the rapid response of hazard-impacted road networksLi Sun, John Shawe-Taylor, Dina D'Ayala
Frontiers in Physiology|September 27, 2021
Predicting T Cell Receptor Antigen Specificity From Structural Features Derived From Homology Models of Receptor-Peptide-Major Histocompatibility ComplexesMartina Milighetti, John Shawe-Taylor, Benny Chain
Frontiers in Physiology|February 10, 2022
Corrigendum: Predicting T Cell Receptor Antigen Specificity From Structural Features Derived From Homology Models of Receptor-Peptide-Major Histocompatibility ComplexesMartina Milighetti, John Shawe-Taylor, Benny Chain
IEEE Transactions on Pattern Analysis and Machine Intelligence|June 23, 2009
Efficient sparse kernel feature extraction based on partial least squaresCharanpal Dhanjal, Steve R Gunn, John Shawe-Taylor
Neural Networks : the Official Journal of the International Neural Network Society|August 1, 1996
Learning in Stochastic Bit Stream Neural NetworksMax van Daalen, John Shawe-Taylor, Jieyu Zhao
Neural Computation|November 2, 2004
Canonical correlation analysis: an overview with application to learning methodsDavid R Hardoon, Sandor Szedmak, John Shawe-Taylor
Entropy (Basel, Switzerland)|October 23, 2021
PAC-Bayes Unleashed: Generalisation Bounds with Unbounded LossesMaxime Haddouche, Benjamin Guedj, Omar Rivasplata, et al.
Journal of Neuroscience Methods|June 30, 2016
A multiple hold-out framework for Sparse Partial Least SquaresJoão M Monteiro, Anil Rao, John Shawe-Taylor, et al.
Bioinformatics (Oxford, England)|January 11, 2013
Decombinator: a tool for fast, efficient gene assignment in T-cell receptor sequences using a finite state machineNiclas Thomas, James Heather, Wilfred Ndifon, et al.
Neuroimage|August 10, 2007
Unsupervised analysis of fMRI data using kernel canonical correlationDavid R Hardoon, Janaina Mourão-Miranda, Michael Brammer, et al.
Pageof 4

Showing results (1-10 of 39) with videos related to

Sort By:
Pageof 4
Scientific Reports|September 29, 2022
Artificial intelligence-informed planning for the rapid response of hazard-impacted road networksLi Sun, John Shawe-Taylor, Dina D'Ayala
Frontiers in Physiology|September 27, 2021
Predicting T Cell Receptor Antigen Specificity From Structural Features Derived From Homology Models of Receptor-Peptide-Major Histocompatibility ComplexesMartina Milighetti, John Shawe-Taylor, Benny Chain
Frontiers in Physiology|February 10, 2022
Corrigendum: Predicting T Cell Receptor Antigen Specificity From Structural Features Derived From Homology Models of Receptor-Peptide-Major Histocompatibility ComplexesMartina Milighetti, John Shawe-Taylor, Benny Chain
IEEE Transactions on Pattern Analysis and Machine Intelligence|June 23, 2009
Efficient sparse kernel feature extraction based on partial least squaresCharanpal Dhanjal, Steve R Gunn, John Shawe-Taylor
Neural Networks : the Official Journal of the International Neural Network Society|August 1, 1996
Learning in Stochastic Bit Stream Neural NetworksMax van Daalen, John Shawe-Taylor, Jieyu Zhao
Neural Computation|November 2, 2004
Canonical correlation analysis: an overview with application to learning methodsDavid R Hardoon, Sandor Szedmak, John Shawe-Taylor
Entropy (Basel, Switzerland)|October 23, 2021
PAC-Bayes Unleashed: Generalisation Bounds with Unbounded LossesMaxime Haddouche, Benjamin Guedj, Omar Rivasplata, et al.
Journal of Neuroscience Methods|June 30, 2016
A multiple hold-out framework for Sparse Partial Least SquaresJoão M Monteiro, Anil Rao, John Shawe-Taylor, et al.
Bioinformatics (Oxford, England)|January 11, 2013
Decombinator: a tool for fast, efficient gene assignment in T-cell receptor sequences using a finite state machineNiclas Thomas, James Heather, Wilfred Ndifon, et al.
Neuroimage|August 10, 2007
Unsupervised analysis of fMRI data using kernel canonical correlationDavid R Hardoon, Janaina Mourão-Miranda, Michael Brammer, et al.
Pageof 4