| Deep learning: An introduction for applied mathematicians CF Higham, DJ Higham Siam review 61 (4), 860-891, 2019 | 297 | 2019 |
| Deep learning for real-time single-pixel video CF Higham, R Murray-Smith, MJ Padgett, MP Edgar Scientific reports 8 (1), 2369, 2018 | 261 | 2018 |
| Somatic instability of the expanded CTG triplet repeat in myotonic dystrophy type 1 is a heritable quantitative trait and modifier of disease severity F Morales, JM Couto, CF Higham, G Hogg, P Cuenca, C Braida, ... Human molecular genetics 21 (16), 3558-3567, 2012 | 191 | 2012 |
| Neural network identification of people hidden from view with a single-pixel, single-photon detector P Caramazza, A Boccolini, D Buschek, M Hullin, CF Higham, ... Scientific reports 8 (1), 11945, 2018 | 95 | 2018 |
| Deep learning optimized single-pixel LiDAR N Radwell, SD Johnson, MP Edgar, CF Higham, R Murray-Smith, ... Applied Physics Letters 115 (23), 2019 | 59 | 2019 |
| High levels of somatic DNA diversity at the myotonic dystrophy type 1 locus are driven by ultra-frequent expansion and contraction mutations CF Higham, F Morales, CA Cobbold, DT Haydon, DG Monckton Human Molecular Genetics 21 (11), 2450-2463, 2012 | 59 | 2012 |
| Controversy in mechanistic modelling with Gaussian processes B Macdonald, C Higham, D Husmeier International conference on machine learning, 1539-1547, 2015 | 43 | 2015 |
| Modelling and inference reveal nonlinear length-dependent suppression of somatic instability for small disease associated alleles in myotonic dystrophy type 1 and Huntington … CF Higham, DG Monckton Journal of The Royal Society Interface 10 (88), 20130605, 2013 | 19 | 2013 |
| Detection, identification, and tracking of objects hidden from view with neural networks G Musarra, P Caramazza, A Turpin, A Lyons, CF Higham, R Murray-Smith, ... Advanced Photon Counting Techniques XIII 10978, 1097803, 2019 | 12 | 2019 |
| A Bayesian approach for parameter estimation in the extended clock gene circuit of Arabidopsis thaliana CF Higham, D Husmeier BMC bioinformatics 14 (Suppl 10), S3, 2013 | 11 | 2013 |
| Bifurcation analysis informs Bayesian inference in the Hes1 feedback loop CF Higham BMC systems biology 3, 1-14, 2009 | 9 | 2009 |
| Controversy in mechanistic modemodel with gaussian processes B Macdonald, CF Higham, D Husmeier International Conference on Machine Learning (ICML) 54, 70, 2015 | 6 | 2015 |
| Quantum deep learning by sampling neural nets with a quantum annealer CF Higham, A Bedford Scientific reports 13 (1), 3939, 2023 | 4 | 2023 |
| Testing a QUBO formulation of core-periphery partitioning on a quantum annealer CF Higham, DJ Higham, F Tudisco arXiv preprint arXiv:2201.01543, 2022 | 3 | 2022 |
| Single-pixel LIDAR with deep learning optimised sampling SD Johnson, N Radwell, MP Edgar, C Higham, R Murray-Smith, ... 2020 Conference on Lasers and Electro-Optics (CLEO), 1-2, 2020 | 3 | 2020 |
| Dynamic DNA and human disease: mathematical modelling and statistical inference for myotonic dystrophy type 1 and Huntington disease CF Higham University of Glasgow, 2013 | 3 | 2013 |
| Core-periphery partitioning and quantum annealing CF Higham, DJ Higham, F Tudisco Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 2 | 2022 |
| Individual variation in the structure of bilingual grammars C Cohen, SW Nabi, CF Higham, M Putnam, GJ Kootstra, JG van Hell Language 97 (4), 752-792, 2021 | 2 | 2021 |
| Deep Learnability: Using Neural Networks to Quantify Language Similarity and Learnability C Cohen, CF Higham, SW Nabi Frontiers in Artificial Intelligence 3, 43, 2020 | 2 | 2020 |
| Diffusion Models for Generative Artificial Intelligence: An Introduction for Applied Mathematicians CF Higham, DJ Higham, P Grindrod arXiv preprint arXiv:2312.14977, 2023 | | 2023 |
