Graham Spinks
PhD Researcher
Biography
Graham was a doctoral researcher at the LIIR Research Lab of the Computer Science Department, Katholieke Universiteit Leuven under the supervision of Prof. dr. Marie-Francine Moens. Before his PhD research, he worked as a data scientist at KBC Bank, where he helped provide insights for e-commerce and worked on hypothesis tests and machine learning algorithms to refine customer targeting. He holds Masters degrees in Electrical Engineering, Mathematical Finance and Artificial Intelligence. Outside of A.I., his interests range from football (soccer) to music to tech news. Currently he works as a Senior Research Scientist at NielsenIQ in Madrid, Spain.
Publications
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Spinks, Graham and Moens, Marie-Francine (August 2019).
Justifying diagnosis decisions by deep neural networks. Journal of Biomedical Informatics , 96. doi: https://doi.org/10.1016/j.jbi.2019.103248
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Spinks, Graham and Moens, Marie-Francine (September 2020).
Structured (De) composable Representations Trained with Neural Networks. Artificial Neural Networks in Pattern Recognition. ANNPR 2020. , 12294. doi: https://doi.org/10.1007/978-3-030-58309-5_3
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Spinks, Graham and Moens, Marie-Francine (October 2020).
Structured (De)composable Representations Trained with Neural Networks. Computers doi: https://doi.org/10.3390/computers9040079
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Spinks, Graham and Cartuyvels, Ruben and Moens, Marie-Francine (October 2020).
Learning Grammar in Confined Worlds. Lecture Notes in Electrical Engineering book series (LNEE) , 704. doi: https://doi.org/10.1007/978-981-15-8395-7_27
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Pavllo, Dario, Graham Spinks, Thomas Hofmann, Marie-Francine Moens, and Aurélien Lucchi (December 2020).
Convolutional Generation of Textured 3D Meshes.. NeurIPS 2020
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Ruben Cartuyvels, Graham Spinks, Marie-Francine Moens (December 2020).
Autoregressive Reasoning over Chains of Facts with Transformers. Proceedings of the 28th International Conference on Computational Linguistics doi: 10.18653/v1/2020.coling-main.610
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Ruben Cartuyvels, Graham Spinks, Marie-Francine Moens (January 2021).
Discrete and continuous representations and processing in deep learning: Looking forward. AI Open , 2. doi: 10.1016/j.aiopen.2021.07.002