no code implementations • 15 Oct 2021 • Marc Roig Vilamala, Tianwei Xing, Harrison Taylor, Luis Garcia, Mani Srivastava, Lance Kaplan, Alun Preece, Angelika Kimmig, Federico Cerutti
We also demonstrate that our approach is capable of training even with a dataset that has a moderate proportion of noisy data.
no code implementations • 5 Mar 2021 • Iain Barclay, Harrison Taylor, Alun Preece, Ian Taylor, Dinesh Verma, Geeth de Mel
Increased adoption of artificial intelligence (AI) systems into scientific workflows will result in an increasing technical debt as the distance between the data scientists and engineers who develop AI system components and scientists, researchers and other users grows.
no code implementations • 27 Oct 2020 • Katie Barrett-Powell, Jack Furby, Liam Hiley, Marc Roig Vilamala, Harrison Taylor, Federico Cerutti, Alun Preece, Tianwei Xing, Luis Garcia, Mani Srivastava, Dave Braines
We present an experimentation platform for coalition situational understanding research that highlights capabilities in explainable artificial intelligence/machine learning (AI/ML) and integration of symbolic and subsymbolic AI/ML approaches for event processing.
BIG-bench Machine Learning Explainable artificial intelligence
no code implementations • 7 Sep 2020 • Marc Roig Vilamala, Harrison Taylor, Tianwei Xing, Luis Garcia, Mani Srivastava, Lance Kaplan, Alun Preece, Angelika Kimmig, Federico Cerutti
We demonstrate this comparing our approach against a pure neural network approach on a dataset based on Urban Sounds 8K.
4 code implementations • 5 Aug 2019 • Liam Hiley, Alun Preece, Yulia Hicks, David Marshall, Harrison Taylor
However, by exploiting a simple technique that removes motion information, we show that it is not the case that this technique is effective as-is for representing relevance in non-image tasks.