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2020 – today
- 2024
[c11]Daniel Cunnington, Mark Law, Jorge Lobo, Alessandra Russo:
The Role of Foundation Models in Neuro-Symbolic Learning and Reasoning. NeSy (1) 2024: 84-100- [i9]Jack Furby, Daniel Cunnington, Dave Braines, Alun D. Preece:
Can we Constrain Concept Bottleneck Models to Learn Semantically Meaningful Input Features? CoRR abs/2402.00912 (2024) - [i8]Daniel Cunnington, Mark Law, Jorge Lobo, Alessandra Russo:
The Role of Foundation Models in Neuro-Symbolic Learning and Reasoning. CoRR abs/2402.01889 (2024) - 2023
- [j1]Daniel Cunnington
, Mark Law, Jorge Lobo, Alessandra Russo:
FFNSL: Feed-Forward Neural-Symbolic Learner. Mach. Learn. 112(2): 515-569 (2023) - [c10]Daniel Cunnington, Mark Law, Jorge Lobo, Alessandra Russo:
Neuro-Symbolic Learning of Answer Set Programs from Raw Data. IJCAI 2023: 3586-3596 - [i7]Jack Furby, Daniel Cunnington, Dave Braines, Alun D. Preece:
Towards a Deeper Understanding of Concept Bottleneck Models Through End-to-End Explanation. CoRR abs/2302.03578 (2023) - [i6]Daniel Cunnington, Flaviu Cipcigan, Rodrigo Neumann Barros Ferreira, Jonathan Booth:
Symbolic Learning for Material Discovery. CoRR abs/2312.11487 (2023) - [i5]Ioannis Mavromatis, Theodoros Spyridopoulos, Pietro Edoardo Carnelli, Woon Hau Chin, Ahmed Khalil, Jennifer Chakravarty, Lucia Cipolina Kun, Robert J. Piechocki, Colin Robbins, Daniel Cunnington, Leigh Chase, Lamogha Chiazor, Chris Preston, Rahul, Aftab Khan:
Cybersecurity in Motion: A Survey of Challenges and Requirements for Future Test Facilities of CAVs. CoRR abs/2312.14687 (2023) - 2022
- [i4]Daniel Cunnington, Mark Law, Jorge Lobo, Alessandra Russo:
Inductive Learning of Complex Knowledge from Raw Data. CoRR abs/2205.12735 (2022) - 2021
- [c9]Daniel Cunnington, Mark Law, Jorge Lobo, Alessandra Russo:
Inductive Learning of Complex Knowledge from Raw Data. TFSOCTAI@AAAI Fall Symposium 2021 - [c8]Daniel Cunnington, Mark Law, Alessandra Russo, Jorge Lobo, Lance M. Kaplan:
Towards Neural-Symbolic Learning to support Human-Agent Operations. FUSION 2021: 1-8 - [i3]Daniel Cunnington, Mark Law, Alessandra Russo, Jorge Lobo:
FF-NSL: Feed-Forward Neural-Symbolic Learner. CoRR abs/2106.13103 (2021) - 2020
- [i2]Daniel Cunnington, Alessandra Russo, Mark Law, Jorge Lobo, Lance M. Kaplan:
NSL: Hybrid Interpretable Learning From Noisy Raw Data. CoRR abs/2012.05023 (2020)
2010 – 2019
- 2019
- [c7]Daniel Cunnington, Mark Law, Alessandra Russo, Elisa Bertino, Seraphin B. Calo:
Towards a Neural-Symbolic Generative Policy Model. IEEE BigData 2019: 4008-4016 - [c6]Elisa Bertino, Graham White, Jorge Lobo, John Ingham, Gregory H. Cirincione, Alessandra Russo, Mark Law, Seraphin B. Calo, Irene Manotas, Dinesh C. Verma, Amani Abu Jabal, Daniel Cunnington, Geeth de Mel:
Generative Policies for Coalition Systems - A Symbolic Learning Framework. ICDCS 2019: 1590-1600 - [c5]Graham White, Daniel Cunnington, Mark Law, Elisa Bertino, Geeth de Mel, Alessandra Russo:
A Comparison Between Statistical and Symbolic Learning Approaches for Generative Policy Models. ICMLA 2019: 1314-1321 - [c4]Daniel Cunnington, Irene Manotas, Mark Law, Geeth de Mel, Seraphin B. Calo, Elisa Bertino, Alessandra Russo:
A Generative Policy Model for Connected and Autonomous Vehicles. ITSC 2019: 1558-1565 - [c3]Daniel Cunnington, Graham White, Mark Law, Geeth de Mel:
A Demonstration of Generative Policy Models in Coalition Environments. PAAMS 2019: 242-245 - [i1]Daniel Cunnington, Graham White, Geeth de Mel:
Synthetic Ground Truth Generation for Evaluating Generative Policy Models. CoRR abs/1904.13233 (2019) - 2018
- [c2]Seraphin B. Calo, Irene Manotas, Geeth de Mel, Daniel Cunnington, Mark Law, Dinesh C. Verma, Alessandra Russo, Elisa Bertino:
AGENP: An ASGrammar-based GENerative Policy Framework. PADG@ESORICS 2018: 3-20 - [c1]Daniel Cunnington, Geeth de Mel, Darren Shaw:
Reduce Cognitive Burden on Drivers through Contextualising Environments. VTC Spring 2018: 1-7
Coauthor Index
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