Richard John Tomsett
Richard John Tomsett
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Zitiert von
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Interpretability of deep learning models: A survey of results
S Chakraborty, R Tomsett, R Raghavendra, D Harborne, M Alzantot, ...
2017 IEEE smartworld, ubiquitous intelligence & computing, advanced …, 2017
Interpretable to whom? A role-based model for analyzing interpretable machine learning systems
R Tomsett, D Braines, D Harborne, A Preece, S Chakraborty
ICML Workshop on Human Interpretability in Machine Learning (WHI 2018), 2018
Stakeholders in explainable AI
A Preece, D Harborne, D Braines, R Tomsett, S Chakraborty
AAAI FSS-18: Artificial Intelligence in Government and Public Sector, 2018
Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX): comparing multi-electrode recordings from simulated and biological mammalian cortical tissue
RJ Tomsett, M Ainsworth, A Thiele, M Sanayei, X Chen, MA Gieselmann, ...
Brain Structure and Function 220 (4), 2333-2353, 2015
100 Questions: identifying research priorities for poverty prevention and reduction
WJ Sutherland, C Goulden, K Bell, F Bennett, S Burall, M Bush, S Callan, ...
Journal of Poverty and Social Justice 21 (3), 189-205, 2013
Sanity checks for saliency metrics
R Tomsett, D Harborne, S Chakraborty, P Gurram, AD Preece
Thirty Fourth AAAI conference on Artificial Intelligence (AAAI-20), 2020
A systematic method to understand requirements for explainable AI (XAI) systems
M Hall, D Harborne, R Tomsett, V Galetic, S Quintana-Amate, A Nottle, ...
Proceedings of the IJCAI Workshop on eXplainable Artificial Intelligence …, 2019
Rapid trust calibration through interpretable and uncertainty-aware AI
R Tomsett, A Preece, D Braines, F Cerutti, S Chakraborty, M Srivastava, ...
Patterns 1 (4), 100049, 2020
Why the failure? How adversarial examples can provide insights for interpretable machine learning
R Tomsett, A Widdicombe, T Xing, S Chakraborty, S Julier, P Gurram, ...
2018 21st International Conference on Information Fusion (FUSION), 838-845, 2018
Integrating learning and reasoning services for explainable information fusion
D Harborne, C Willis, R Tomsett, AD Preece
Distributed opportunistic sensing and fusion for traffic congestion detection
A Nottle, D Harborne, D Braines, M Alzantot, S Quintana-Amate, ...
2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced …, 2017
A deep convolutional network for traffic congestion classification
C Willis, D Harborne, R Tomsett, M Alzantot
Proc NATO IST-158/RSM-010 Specialists' Meeting on Content Based Real-Time …, 2017
Model poisoning attacks against distributed machine learning systems
R Tomsett, K Chan, S Chakraborty
Artificial Intelligence and Machine Learning for Multi-Domain Operations …, 2019
Neural networks at the edge
D Roy, G Srinivasan, P Panda, R Tomsett, N Desai, R Ganti, K Roy
2019 IEEE International Conference on Smart Computing (SMARTCOMP), 45-50, 2019
Demonstration of dynamic distributed orchestration of node-RED IoT workflows using a vector symbolic architecture
R Tomsett, G Bent, C Simpkin, I Taylor, D Harbourne, A Preece, R Ganti
2019 IEEE International Conference on Smart Computing (SMARTCOMP), 464-467, 2019
Reasoning and learning services for coalition situational understanding
D Harborne, R Raghavendra, C Willis, S Chakraborty, P Dewan, ...
Ground/Air Multisensor Interoperability, Integration, and Networking for …, 2018
Explaining motion relevance for activity recognition in video deep learning models
L Hiley, A Preece, Y Hicks, S Chakraborty, P Gurram, R Tomsett
arXiv preprint arXiv:2003.14285, 2020
Illuminated Decision Trees with Lucid
D Mott, R Tomsett
BMVC 2019: Workshop on Interpretable and Explainable Machine Vision, 2019
Uncertainty-aware situational understanding
R Tomsett, L Kaplan, F Cerutti, P Sullivan, D Vente, MR Vilamala, ...
Artificial Intelligence and Machine Learning for Multi-Domain Operations …, 2019
Supporting User Fusion of AI Services through Conversational Explanations
D Braines, R Tomsett, A Preece
FUSION, 2019
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