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Gareth John Conduit
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2020 – today
- 2024
- [j9]Zhenpei Wang
, Brian N. Cox, Shemuel Joash Kuehsamy, Mark Hyunpong Jhon, Olivier Sudre, N. Sridhar, Gareth John Conduit:
A compact yet flexible design space for large-scale nonperiodic 3D woven composites based on a weighted game for generating candidate tow architectures. Comput. Aided Des. 167: 103637 (2024) - [j8]Yogesh K. Dwivedi, Laurie Hughes, Harshad K. D. H. Bhadeshia, Sophia Ananiadou, Anthony G. Cohn, Jacqueline M. Cole, Gareth John Conduit, Maunendra Sankar Desarkar, Xinwei Wang:
Artificial intelligence (AI) futures: India-UK collaborations emerging from the 4th Royal Society Yusuf Hamied workshop. Int. J. Inf. Manag. 76: 102725 (2024) - [j7]Thomas M. Whitehead
, Joel Strickland, Gareth John Conduit, Alexandre Borrel, Daniel Mucs, Irene Baskerville-Abraham:
Quantifying the Benefits of Imputation over QSAR Methods in Toxicology Data Modeling. J. Chem. Inf. Model. 64(7): 2624-2636 (2024) - 2023
- [j6]Bahdan Zviazhynski
, Gareth John Conduit:
Unveil the unseen: Exploit information hidden in noise. Appl. Intell. 53(10): 11966-11978 (2023) - [i5]Jessica C. Forsdyke, Bahdan Zviazhynski, Janet M. Lees, Gareth John Conduit:
Probabilistic selection and design of concrete using machine learning. CoRR abs/2304.11226 (2023) - 2022
- [i4]Bahdan Zviazhynski, Gareth John Conduit:
Unveil the unseen: Exploit information hidden in noise. CoRR abs/2209.08376 (2022) - 2021
- [j5]Samar Mahmoud, Benedict W. J. Irwin, Dmitriy Chekmarev, Shyam Vyas
, Jeff Kattas, Thomas M. Whitehead
, Tamsin Mansley, Jack Bikker, Gareth John Conduit, Matthew D. Segall
:
Imputation of sensory properties using deep learning. J. Comput. Aided Mol. Des. 35(11): 1125-1140 (2021) - 2020
- [j4]Benedict W. J. Irwin
, Julian R. Levell
, Thomas M. Whitehead
, Matthew D. Segall
, Gareth John Conduit:
Practical Applications of Deep Learning To Impute Heterogeneous Drug Discovery Data. J. Chem. Inf. Model. 60(6): 2848-2857 (2020) - [j3]Man-Fai Ng, Jin Zhao, Qingyu Yan, Gareth John Conduit, Zhi Wei Seh
:
Predicting the state of charge and health of batteries using data-driven machine learning. Nat. Mach. Intell. 2(3): 161-170 (2020) - [j2]Yu Yang Fredrik Liu, Yin Lu
, Steve Oh, Gareth John Conduit
:
Machine learning to predict mesenchymal stem cell efficacy for cartilage repair. PLoS Comput. Biol. 16(10) (2020)
2010 – 2019
- 2019
- [j1]Thomas M. Whitehead
, Benedict W. J. Irwin
, Peter A. Hunt
, Matthew D. Segall
, Gareth John Conduit:
Imputation of Assay Bioactivity Data Using Deep Learning. J. Chem. Inf. Model. 59(3): 1197-1204 (2019) - [i3]John Armitage, Leszek J. Spalek, Malgorzata Nguyen, Mark Nikolka, Ian E. Jacobs
, Lorena Marañón, Iyad Nasrallah, Guillaume Schweicher, Ivan Dimov, Dimitrios Simatos
, Iain McCulloch
, Christian B. Nielsen, Gareth John Conduit, Henning Sirringhaus:
Fragment Graphical Variational AutoEncoding for Screening Molecules with Small Data. CoRR abs/1910.13325 (2019) - 2018
- [i2]Bryce D. Conduit, Nick G. Jones, Howard J. Stone, Gareth John Conduit:
Probabilistic design of a molybdenum-base alloy using a neural network. CoRR abs/1803.00879 (2018) - [i1]Bryce D. Conduit, Nick G. Jones, Howard J. Stone, Gareth John Conduit:
Design of a nickel-base superalloy using a neural network. CoRR abs/1803.03039 (2018)
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