Scott E. Fahlman

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Christian LebiereResearch Faculty, Psychology Department, Carnegie Mellon UniversityVerified email at cmu.edu
Geoffrey HintonEmeritus Prof. Comp Sci, U.Toronto & Engineering Fellow, GoogleVerified email at cs.toronto.edu
Terrence SejnowskiFrancis Crick Professor, Salk Institute, Distingished Professor, UC San DiegoVerified email at salk.edu
Douglas FisherVanderbilt UniversityVerified email at vanderbilt.edu
Sebastian ThrunStanfordVerified email at stanford.edu
Tom MitchellFounders University Professor of Machine Learning, Carnegie Mellon UniversityVerified email at cs.cmu.edu
David TouretzkyProfessor of Computer Science, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Juan Carlos LópezProfessor of Computer Architecture, University of Castilla-La ManchaVerified email at uclm.es
Francisco MoyaUniversity of Castilla-La Mancha, School of Industrial and Aerospace EngineeringVerified email at uclm.es
Rahul SukthankarGoogle ResearchVerified email at google.com
Shumeet BalujaGoogle, Inc.Verified email at google.com
Benjamin W. WahChinese University of Hong KongVerified email at illinois.edu

Scott E. Fahlman

Professor Emeritus, Language Technologies and Computer Science, Carnegie Mellon University

Verified email at cs.cmu.edu - Homepage

Artificial Intelligence Knowledge Representation and Reasoning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
The cascade-correlation learning architecture S Fahlman, C Lebiere Advances in neural information processing systems 2, 1989	4529	1989
Faster learning variations of back propagation: An empirical study SE Fahlman Proceedings of the 1988 connectionist models summer school, 38-51, 1988	1785	1988
An empirical study of learning speed in back-propagation networks SE Fahlman Carnegie Mellon University, Computer Science Department, 1988	1401	1988
NETL: A system for representing and using real-world knowledge SE Fahlman American Journal of Computational Linguistics 6 (3-4), 1980	885	1980
The monk's problems: A performance comparison of different learning algorithms SB Thrun, JW Bala, E Bloedorn, I Bratko, B Cestnik, J Cheng, KA De Jong, ...	638	1991
A planning system for robot construction tasks SE Fahlman Artificial intelligence 5 (1), 1-49, 1974	344	1974
The recurrent cascade-correlation architecture S Fahlman Advances in neural information processing systems 3, 1990	328	1990
Massively parallel architectures for Al: NETL, Thistle, and Boltzmann machines SE Fahlman, GE Hinton, TJ Sejnowski National Conference on Artificial Intelligence, AAAI, 1983	315	1983
Learning with limited numerical precision using the cascade-correlation algorithm M Hoehfeld, SE Fahlman Carnegie-Mellon University. Department of Computer Science, 1991	294	1991
Connectionist architectures for artificial intelligence SE Fahlman, GE Hinton Computer 20 (01), 100-109, 1987	248	1987
Method for transforming message containing sensitive information SE Fahlman, R Sukthankar, A Brusseau US Patent 5,960,080, 1999	176	1999
Advances in neural information processing systems SE Fahlman, C Lebiere, D Touretzky	125	1990
A system for representing and using real-world knowledge SE Fahlman	93	1977
Cancellation in a Parallel Semantic Network. SE Fahlman, DS Touretzky, W van Roggen Ijcai, 257-263, 1981	87	1981
Proceedings of the national conference on artificial intelligence SE Fahlman, GE Hinton, TJ Sejnowski	85	1983
Reducing network depth in the cascade-correlation learning architecture S Baluja, SE Fahlman CARNEGIE-MELLON UNIV PITTSBURGH PA SCHOOL OF COMPUTER SCIENCE, 1994	82	1994
The cascade-correlation learning algorithm SE Fahlman Advances in neural information processing systems 2, 525-532, 1990	77	1990
Representing implicit knowledge SE Fahlman Parallel models of associative memory, 181-200, 2014	68	2014
Marker-passing inference in the scone knowledge-base system SE Fahlman Knowledge Science, Engineering and Management: First International …, 2006	68	2006
Smiley lore:- SE Fahlman Retrieved October 6, 2002, 2002	68	2002

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