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A Priori Sub-grid Modelling Using Artificial Neural Networks
Published in International Journal of Computational Fluid Dynamics, 2020
This paper concerns the use of artificial neural networks to close the turbulence model in Large Eddy Simulations. You can find more information on the project by clicking on the paper title above, or reading the paper below.
Recommended citation: Alvaro Prat, Theophile Sautory & S. Navarro-Martinez (2020): A Priori Sub-grid Modelling Using Artificial Neural Networks, International Journal of Computational Fluid Dynamics, DOI: 10.1080/10618562.2020.1789116. https://www.tandfonline.com/doi/abs/10.1080/10618562.2020.1789116?journalCode=gcfd20
HySTER: A Hybrid Spatio-Temporal Event Reasoner
Published in Thirty- Fifth AAAI Conference on Artificial Intelligence Workshop on Hybrid Artificial Intelligence, 2021
This paper presents a novel neuro-symbolic appraoch to video question answering, performing perception tasks with deep learning and temporal and causal reasoning with inductive logic programming. You can find more information on the project by clicking on the paper title above, or reading the paper below.
Recommended citation: Theophile Sautory, Nuri Cingillioglu, Alessandra Russo (2021): HySTER: A Hybrid Spatio-Temporal Event Reasoner, Thirty- Fifth AAAI Conference on Artificial Intelligence Workshop on Hybrid Artificial Intelligence. https://arxiv.org/pdf/2101.06644.pdf
Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspection
Published in SPIE Optical Metrology. Proceedings Volume 11787, Automated Visual Inspection and Machine Vision IV, 2021
This paper studies the robustness of using deep features from pre-trained networks on low data regimes anomaly detection tasks.
Recommended citation: Pierre Gutierrez, Antoine Cordier, Thaïs Caldeira, Theophile Sautory (2021): Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspection, Proc. SPIE 11787, Automated Visual Inspection and Machine Vision IV, 1178703, DOI: 10.1117/12.2591876. https://arxiv.org/pdf/2106.01277.pdf
Learning to Rank the Distinctiveness of Behaviour in Serial Offending
Published in International Conference on Logic Programming and Nonmonotonic Reasoning, 2022
This paper presents the first learning based approach aimed at identifying contexts within which behaviour may be considered distinctive in the context of comparative case analysis used to detect serial offending.
Recommended citation: M Law, T Sautory, L Mitchener, K Davies, M Tonkin, J Woodhams, D Alrajeh (2022): Learning to Rank the Distinctiveness of Behaviour in Serial Offending, International Conference on Logic Programming and Nonmonotonic Reasoning. https://link.springer.com/chapter/10.1007/978-3-031-15707-3_37