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Catherine F Higham

Showing results (1-10 of 9) with videos related to

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BMC Systems Biology|January 28, 2009
Bifurcation analysis informs Bayesian inference in the Hes1 feedback loopCatherine F Higham
Scientific Reports|March 9, 2023
Quantum deep learning by sampling neural nets with a quantum annealerCatherine F Higham, Adrian Bedford
BMC Bioinformatics|November 26, 2013
A Bayesian approach for parameter estimation in the extended clock gene circuit of Arabidopsis thalianaCatherine F Higham, Dirk Husmeier
Journal of the Royal Society, Interface|September 20, 2013
Modelling and inference reveal nonlinear length-dependent suppression of somatic instability for small disease associated alleles in myotonic dystrophy type 1 and Huntington diseaseCatherine F Higham, Darren G Monckton
Frontiers in Artificial Intelligence|March 18, 2021
Deep Learnability: Using Neural Networks to Quantify Language Similarity and LearnabilityClara Cohen, Catherine F Higham, Syed Waqar Nabi
Scientific Reports|February 7, 2018
Deep learning for real-time single-pixel videoCatherine F Higham, Roderick Murray-Smith, Miles J Padgett, et al.
Human Molecular Genetics|February 28, 2012
High levels of somatic DNA diversity at the myotonic dystrophy type 1 locus are driven by ultra-frequent expansion and contraction mutationsCatherine F Higham, Fernando Morales, Christina A Cobbold, et al.
Scientific Reports|August 11, 2018
Neural network identification of people hidden from view with a single-pixel, single-photon detectorPiergiorgio Caramazza, Alessandro Boccolini, Daniel Buschek, et al.
Human Molecular Genetics|May 19, 2012
Somatic instability of the expanded CTG triplet repeat in myotonic dystrophy type 1 is a heritable quantitative trait and modifier of disease severityFernando Morales, Jillian M Couto, Catherine F Higham, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
BMC Systems Biology|January 28, 2009
Bifurcation analysis informs Bayesian inference in the Hes1 feedback loopCatherine F Higham
Scientific Reports|March 9, 2023
Quantum deep learning by sampling neural nets with a quantum annealerCatherine F Higham, Adrian Bedford
BMC Bioinformatics|November 26, 2013
A Bayesian approach for parameter estimation in the extended clock gene circuit of Arabidopsis thalianaCatherine F Higham, Dirk Husmeier
Journal of the Royal Society, Interface|September 20, 2013
Modelling and inference reveal nonlinear length-dependent suppression of somatic instability for small disease associated alleles in myotonic dystrophy type 1 and Huntington diseaseCatherine F Higham, Darren G Monckton
Frontiers in Artificial Intelligence|March 18, 2021
Deep Learnability: Using Neural Networks to Quantify Language Similarity and LearnabilityClara Cohen, Catherine F Higham, Syed Waqar Nabi
Scientific Reports|February 7, 2018
Deep learning for real-time single-pixel videoCatherine F Higham, Roderick Murray-Smith, Miles J Padgett, et al.
Human Molecular Genetics|February 28, 2012
High levels of somatic DNA diversity at the myotonic dystrophy type 1 locus are driven by ultra-frequent expansion and contraction mutationsCatherine F Higham, Fernando Morales, Christina A Cobbold, et al.
Scientific Reports|August 11, 2018
Neural network identification of people hidden from view with a single-pixel, single-photon detectorPiergiorgio Caramazza, Alessandro Boccolini, Daniel Buschek, et al.
Human Molecular Genetics|May 19, 2012
Somatic instability of the expanded CTG triplet repeat in myotonic dystrophy type 1 is a heritable quantitative trait and modifier of disease severityFernando Morales, Jillian M Couto, Catherine F Higham, et al.
Pageof 1