Alireza Tamaddoni-Nezhad

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Stephen MuggletonImperial College LondonVerified email at imperial.ac.uk
Alan RaybouldInnogen Institute, University of EdinburghVerified email at ed.ac.uk
Dianhuan LinZscalerVerified email at zscaler.com
Dr. Corinne VACHERResearcher in Ecology, INRAE, FranceVerified email at inrae.fr
Antonis KakasUniversity of CyprusVerified email at ucy.ac.cy
Guy WoodwardProfessor of Ecology, Department of Life Sciences, Imperial College LondonVerified email at imperial.ac.uk
Tarek R. BesoldSony AIVerified email at sony.com
Ute SchmidProfessor of Cognitive Systems, University of BambergVerified email at uni-bamberg.de
Raphael ChaleilComputational Protein Engineer, Basecamp ResearchVerified email at basecamp-research.com
Darren M. EvansProfessor of Ecology and Conservation, Newcastle UniversityVerified email at ncl.ac.uk
Michael PocockNERC Centre for Ecology & HydrologyVerified email at ceh.ac.uk
Wang-Zhou Dai (戴望州)Nanjing University, Suzhou CampusVerified email at nju.edu.cn
Zhi-Hua ZhouNanjing UniversityVerified email at nju.edu.cn
M SternbergImperial College LondonVerified email at imperial.ac.uk
Alex J. DumbrellLecturer in Environmental Microbiology and Bioinformatics, University of EssexVerified email at essex.ac.uk
Christian MulderUniversity of CataniaVerified email at unict.it
François MassolIEES CNRS ParisVerified email at cnrs.fr
Brendan WrenProfessor of Pathogen Molecular BiologyVerified email at lshtm.ac.uk
Claude SammutProfessor of Computer Science and Engineering, University of New South WalesVerified email at cse.unsw.edu.au
Andrew CropperUniversity of OxfordVerified email at cs.ox.ac.uk

Alireza Tamaddoni-Nezhad

Reader (Associate Professor), University of Surrey

Verified email at surrey.ac.uk - Homepage

Relational Learning Inductive Logic Programming Machine Learning Automation of Science Sustainable AI


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Inductive logic programming S Muggleton, R Otero, A Tamaddoni-Nezhad Academic Press, 2006	1005*	2006
Meta-interpretive learning of higher-order dyadic datalog: Predicate invention revisited SH Muggleton, D Lin, A Tamaddoni-Nezhad Machine Learning 100 (1), 49-73, 2015	293	2015
Next-generation global biomonitoring: large-scale, automated reconstruction of ecological networks DA Bohan, C Vacher, A Tamaddoni-Nezhad, A Raybould, AJ Dumbrell, ... Trends in ecology & evolution 32 (7), 477-487, 2017	212	2017
Meta-interpretive learning: application to grammatical inference SH Muggleton, D Lin, N Pahlavi, A Tamaddoni-Nezhad Machine learning 94, 25-49, 2014	176	2014
Ultra-strong machine learning: comprehensibility of programs learned with ILP SH Muggleton, U Schmid, C Zeller, A Tamaddoni-Nezhad, T Besold Machine Learning 107, 1119-1140, 2018	138	2018
Learning ecological networks from next-generation sequencing data C Vacher, A Tamaddoni-Nezhad, S Kamenova, N Peyrard, Y Moalic, ... Advances in ecological research 54, 1-39, 2016	130	2016
Networking agroecology: integrating the diversity of agroecosystem interactions DA Bohan, A Raybould, C Mulder, G Woodward, A Tamaddoni-Nezhad, ... Advances in ecological research 49, 1-67, 2013	117	2013
Application of abductive ILP to learning metabolic network inhibition from temporal data A Tamaddoni-Nezhad, R Chaleil, A Kakas, S Muggleton Machine Learning 64, 209-230, 2006	114	2006
Networking our way to better ecosystem service provision Quintessence Consortium Trends in Ecology & Evolution 31 (2), 105-115, 2016	105	2016
Key questions for next-generation biomonitoring A Makiola, ZG Compson, DJ Baird, MA Barnes, SP Boerlijst, A Bouchez, ... Frontiers in Environmental Science 7, 197, 2020	99	2020
Automated discovery of food webs from ecological data using logic-based machine learning DA Bohan, G Caron-Lormier, S Muggleton, A Raybould, ... PLoS One 6 (12), e29028, 2011	95	2011
The visualisation of ecological networks, and their use as a tool for engagement, advocacy and management MJO Pocock, DM Evans, C Fontaine, M Harvey, R Julliard, Ó McLaughlin, ... Advances in ecological research 54, 41-85, 2016	86	2016
Progolem: A system based on relative minimal generalisation S Muggleton, J Santos, A Tamaddoni-Nezhad International Conference on Inductive Logic Programming, 131-148, 2009	68	2009
Construction and validation of food webs using logic-based machine learning and text mining A Tamaddoni-Nezhad, GA Milani, A Raybould, S Muggleton, DA Bohan Advances in Ecological Research 49, 225-289, 2013	65	2013
How does predicate invention affect human comprehensibility? U Schmid, C Zeller, T Besold, A Tamaddoni-Nezhad, S Muggleton Inductive Logic Programming: 26th International Conference, ILP 2016, London …, 2017	55	2017
TopLog: ILP using a logic program declarative bias SH Muggleton, JCA Santos, A Tamaddoni-Nezhad International Conference on Logic Programming, 687-692, 2008	51	2008
The lattice structure and refinement operators for the hypothesis space bounded by a bottom clause A Tamaddoni-Nezhad, S Muggleton Machine learning 76, 37-72, 2009	46	2009
QG/GA: a stochastic search for Progol S Muggleton, A Tamaddoni-Nezhad Machine Learning 70, 121-133, 2008	43	2008
Meta-interpretive learning from noisy images S Muggleton, WZ Dai, C Sammut, A Tamaddoni-Nezhad, J Wen, ZH Zhou Machine Learning 107, 1097-1118, 2018	41	2018
Ecological networks reveal resilience of agro-ecosystems to changes in farming management A Ma, X Lu, C Gray, A Raybould, A Tamaddoni-Nezhad, G Woodward, ... Nature Ecology & Evolution 3 (2), 260-264, 2019	37	2019

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