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H.T. Siegelmann Neural Networks and Analog Computation: Beyond the Turing Limit, Birkhauser Boston, December 1998 More information here |
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Prof. Siegelmann's Theses Ph.D. Thesis: "Foundation of Recurrent Neural Networks," Rutgers University, 1993 Master Thesis: "Document Allocation in Multiprocessor Information Retrieval Systems: An Application of Genetic Algorithms," The Hebrew University, 1992 |
| Type | Author | Title | Publication Information | Date | Pages | Links |
|---|---|---|---|---|---|---|
| Jrn | K. Tu, D. Cooper, H.T. Siegelmann | Memory reconsolidation for natural language processing | Cogn Neurodyn 3 | 2009 | 365-372 | |
| Jrn | A. Z. Pietrzykowski, R. M. Friesen, G. E. Martin, S.I. Puig, C. L. Nowak, P. M. Wynne, H. T. Siegelmann, S. N. Treistman | Post-transcriptional regulation of BK channel splice variant stability by miR-9 underlies neuroadaptation to alcohol | Neuron 59(2) | 2008 | 274-287 | |
| Jrn | Lu, S., Becker, K.A., Hagen, M.J., Yan, H., Roberts, A.L., Mathews, L.A., Schneider, S.S., Siegelmann, H.T., Tirrell, S.M., MacBeth, K.J., Blanchard, J.L. and Jerry, D.J. | Transcriptional responses to estrogen and progesterone in Mammary gland identify networks regulating p53 activity | Endocrinology | 2008 | - | |
| Jrn | H.T. Siegelmann | Analog-Symbolic Memory that Tracks via Reconsoliation | Physica D 237(9) | 2008 | 1207-1214 | |
| Jrn | M.M. Olsen, N. Siegelmann-Danieli and H.T. Siegelmann | Robust Artificial Life Via Artificial Programmed Death | Artificial Intelligence 172(6-7) | 2008 | 884-898 | |
| Jrn | F. Roth, H.T. Siegelmann and R. J. Douglas | The Self-Construction and -Repair of a Foraging Organism by Explicitly Specified Development from a Single Cell | Artificial Life 13(4) | 2007 | 347-368 | abstract |
| Jrn | S. Sivan, O. Filo and H. Siegelman | Application of Expert Networks for Predicting Proteins Secondary Structure | Biomolecular Engineering, Volume 24, Issue 2 | 2007 | 237-243 | |
| Jrn | W. Bush and H.T. Siegelmann | Circadian Synchrony in Networks of Protein Rhythm Driven Neurons | Complexity Volume 12, Issue 1 | 2006 | 67-72 | |
| Jrn | T. Leise and H Siegelmann | Dynamics of a multistage circadian system | Journal of Biological Rhythms, 21:4 | 2006 | 314-323 | |
| Jrn | L. Glass, T. J. Perkins, J. Mason, H. T. Siegelmann and R. Edwards | Chaotic Dynamics in an Electronic Model of a Genetic Network | Journal of Statistical Physics Volume 121 Numbers 5/6 | 2005 | 969-994 | |
| Jrn | Loureiro, O. and Siegelmann, H. | Introducing an Active Cluster-Based Information Retrieval Paradigm | Journal of the American Society for Information Science and Technology, vl 56, n. 10 | 2005 | 1024-1030 | |
| Jrn | A. Roitershtein, A. Ben-Hur and H.T. Siegelmann | On Probabilistic Analog Automata | Theoretical Computer Science, 320(2-3) | 2004 | 449-464 | |
| Jrn | A. Ben-Hur, H.T. Siegelmann | Computation in Gene Networks | Chaos: An Interdisciplinary Journal of Nonlinear Science, 14(1) | 2004 | 145-151 | |
| Jrn | A. Ben-Hur, J. Feinberg, S. Fishman and H. T. Siegelmann | Random matrix theory for the analysis of the performance of an analog computer: a scaling theory | Phys. Lett. A. 323(3-4) | 2004 | 204-209 | |
| Jrn | A. Ben-Hur, J. Feinberg, S. Fishman and H. T. Siegelmann | Probabilistic analysis of a differential equation for linear programming | Journal of Complexity, 19(4) | 2003 | 474-510 | |
| Jrn | J. P. Neto, H. T. Siegelmann, and J. F. Costa | Symbolic processing in neural networks | Journal of the Brazilian Computer Society, 8(3) | 2003 | - | |
| Jrn | S. Eldar, H. T. Siegelmann, D. Buzaglo, I. Matter, A. Cohen, E. Sabo, J. Abrahamson | Conversion of Laparoscopic Cholecystectomy to open cholecystectomy in acute cholecystitis: Artificial neural networks improve the prediction of conversion | World Journal of Surgery, 26(1) | 2002 | 79-85 | |
| Jrn | A. Ben-Hur, H.T. Siegelmann and S. Fishman. | A Theory of Complexity for Continuous Time Systems. | Journal of Complexity 18(1) | 2002 | 51-86 | |
| Jrn | H.T. Siegelmann | Neural and Super-Turing Computing | Minds and Machines 13(1) | 2003 | 103-114 | |
| Jrn | A. Ben-Hur, D. Horn, H.T. Siegelmann and V. Vapnik | Support Vector Clustering | Journal of Machine Learning Research 2 | 2001 | 125-137 | |
| Jrn | H.T. Siegelmann A., Ben-Hur, S. Fishman | Comments on Attractor Computation | International Journal of Computing Anticipatory Systems, D.M. Dubois, ed. | 2000 | - | |
| Jrn | R. Edwards, H.T. Siegelmann, K. Aziza and L. Glass | Symbolic dynamics and computation in model gene networks | Chaos 11(1) | 2001 | 160-169 | |
| Jrn | H. Lipson and H.T. Siegelmann | Clustering Irregular Shapes Using High-Order Neurons | Neural Computation 12(10) | 2000 | 2331-2353 | |
| Jrn | D. Lange, H.T. Siegelmann, H. Pratt, and G.F. Inbar | Overcoming Selective Ensemble Averaging: Unsupervised Identification of Event-Related Brain Potentials | IEEE Transactions on Biomedical Engineering, 47(6) | 2000 | 822-826 | |
| Jrn | H. Karniely and H.T. Siegelmann | Sensor Registration Using Neural Networks | IEEE Transactions on Aerospace and Electronic Systems, 36(1) | 2000 | 85-98 | |
| Jrn | H.T. Siegelmann, | Stochastic Analog Networks and Computational Complexity | Journal of Complexity, 15(4) | 1999 | 451-475 | |
| Jrn | H.T. Siegelmann, A. Ben-Hur and S. Fishman | Computational Complexity for Continuous Time Dynamics | Physical Review Letters, 83(7) | 1999 | 1463-1466 | |
| Jrn | H.T. Siegelmann and M. Margenstern | Nine Switch-Affine Neurons Suffice for Turing Universality | Neural Networks, 12 | 1999 | 593-600 | |
| Jrn | R. Gavaldà and H.T. Siegelmann | Discontinuities in Recurrent Neural Networks | Neural Computation, 11(3) | 1999 | 715-745 | |
| Jrn | H.T. Siegelmann and S. Fishman | Analog Computation with Dynamical Systems | Physica D, 120 | 1998 | 120--214 | |
| Jrn | A. Galperin, Y. Kimhi, E. Nissan, and H.T. Siegelmann | FUELCON's Heuristics, their Rationale, and their Representations | The New Review of Applied Expert Systems, 4 | 1998 | 163-176 | abstract |
| Jrn | Joachim Utans, John Moody, Steve Rehfuss, and Hava Siegelmann | Input Variable Selection for Neural Networks: Application to Predicting the U.S. Business Cycle | Proceedings of Computational Intelligence in Financial Engineering, IEEE Press | 1995 | 118-122 | |
| Jrn | H.T. Siegelmann, E. Nissan, and A. Galperin | A Novel Neural/Symbolic Hybrid Approach to Heuristically Optimized Fuel Allocation and Automated Revision of heuristics in Nuclear Engineering | Advances in Engineering Software, 28(9) | 1997 | 581-592 | |
| Jrn | J.L. Balcázar, R. Gavaldà, and H.T. Siegelmann | Computational Power of Neural Networks: A Characterization in Terms of Kolmogorov Complexity | IEEE Transactions on Information Theory, 43(4) | 1997 | 1175-1183 | |
| Jrn | H.T. Siegelmann, B.G. Horne, and C.L.Giles | Computational Capabilities of Recurrent NARX Neural Networks | IEEE Transaction on Systems, Man and Cybernetics-part B: Cybernetics, 27(2) | 1997 | 208-215 | |
| Jrn | E. Nissan, H.T. Siegelmann, A. Galperin, and S. Kimhi, | Upgrading Automation for Nuclear Fuel In-Core Management: From the Symbolic Generation of Configurations, to the Neural Adaptation of Heuristics, | Engineering with Computers, 13(1) | 1997 | 1-19 | |
| Jrn | O. Frieder and H.T. Siegelmann | Multiprocessor Document Allocation: A Genetic Algorithm Approach | IEEE Transactions on Knowledge and Data Engineering, 9(4) | 1997 | 640-642 | |
| Jrn | H.T. Siegelmann and C.L. Giles | The Complexity of Language Recognition by Neural Networks | Journal of Neurocomputing; Special Issue Recurrent Networks for Sequence Processing, Editors: M. Gori, M. Mozer, A.H. Tsoi, W. Watrous, 15(3-4) | 1997 | 327-345 | |
| Jrn | H.T. Siegelmann, | On NIL: The Software Constructor of Neural Networks | Parallel Processing Letters, 6(4) | 1996 | 575-582 | abstract |
| Jrn | H.T. Siegelmann | The Simple Dynamics of Super Turing Theories | Theoretical Computer Science, 168(2)(special issue on UMC) | 1996 | 461-472 | |
| Jrn | H.T. Siegelmann | Recurrent Neural Networks and Finite Automata | Journal of Computational Intelligence, 12(4) | 1996 | 567-574 | abstract |
| Jrn | J. Kilian and H.T. Siegelmann | The Dynamic Universality of Sigmoidal Neural Networks | Information and Computation, 128(1) | 1996 | 45-56 | |
| Jrn | H.T. Siegelmann | Analog Computational Power | Science, 271(19) | 1996 | - | |
| Jrn | B. DasGupta, H.T. Siegelmann and E. Sontag | On the Complexity of Training Neural Networks with Continuous Activation Functions | IEEE Transactions on Neural Networks, 6(6) | 1995 | 1490-1504 | |
| Jrn | H.T. Siegelmann | Computation Beyond the Turing Limit | Science, 238(28) | 1995 | - | |
| Jrn | H.T. Siegelmann and E.D. Sontag | On the Computational Power of Neural Nets, | Journal of Computer System Sciences, 50(1) | 1995 | 132-150 | |
| Jrn | H.T. Siegelmann and E.D. Sontag | Analog Computation via Neural Networks | Theoretical Computer Science, 131 | 1994 | 331-360 | |
| Jrn | H.T. Siegelmann and E.D. Sontag | Turing Computability with Neural Nets | Applied Mathematics Letters, 4(6) | 1991 | 77-80 | |
| Cpt | K.I. Harrington and H.T. Siegelmann | Adaptive Multi-modal Sensors | 50 Years of Artificial Intelligence, eds. M. Lungarella, F. Iida, J. Bongard, R. Pfeifer | 2007 | 164-173 | |
| Cpt | Bhaskar DasGupta, Derong Liu and Hava Siegelmann | Neural Networks | Handbook on Approximation Algorithms and Metaheuristics, ed. Teofilo F. Gonzalez, Chapman & Hall/CRC (Computer & Information Science Series, ed. Sartaj Sahni) | 2007 | 22.1-22.14 | - |
| Cpt | H. T. Siegelmann | Neural Computing | New Trends in Computer Science, ed. Gheroge Paul | 2003 | - | - |
| Cpt | H.T. Siegelmann | Neural Automata and Computational Complexity | in Handbook of Brain Theory and Neural Networks, ed. Michael A. Arbib | 2002 | - | - |
| Cpt | H.T. Siegelmann | Universal Computation and Super-Turing Capabilities | Field Guide to Dynamical Recurrent Networks, eds. S.C. Kremer and J.F. Kolen, IEEE Press | 2000 | - | - |
| Cpt | H.T. Siegelmann | Finite vs. Infinite Descriptive Length in Neural Networks and the Associated Computational Complexity | Finite vs. Infinite: Contributions to an Eternal Dilemma, eds. C. Calude and Gh. Paun, Springer Verlag | 2000 | - | - |
| Cpt | H.T. Siegelmann | Neural Automata and Computational Complexity | Handbook of Brain Theory and Neural Networks, ed. Michael A. Arbib | 2000 | - | - |
| Cpt | H. Lipson and H.T. Siegelmann | High Order Eigentensors as Symbolic Rules in Competitive Learning | Hybrid Neural Symbolic Integration, eds. S. Wermter and R. Sun, Springer | 1999 | - | - |
| Cpt | H.T. Siegelmann | Neural Dynamics with Stochasticity | Adaptive Processing of Sequences and Data Structures, eds. C.L. Giles and M. Gori, Springer | 1998 | 346-369 | |
| Cpt | H.T. Siegelmann | Computability with Neural Networks | Lectures in Applied Mathematics, Vol. 32, J. Reneger, eds. M. Shub, and S. Smale, American Mathematical Society | 1996 | 733-747 | - |
| Cpt | H.T. Siegelmann | Neural Automata | Shape, Structures and Pattern Recognition, eds. D. Dori and F. Bruckstein, World Scientific | 1995 | - | - |
| Cpt | H.T. Siegelmann | Towards a Neural Programming Language | Shape, Structures and Pattern Recognition, eds. D. Dori and F. Bruckstein, World Scientific | 1995 | - | - |
| Cpt | H.T. Siegelmann | Recurrent Neural Networks | The 1000th Volume of Lecture Notes in Computer Science: Computer Science Today, ed. Jan Van Leeuwen, Springer Verlag | 1995 | 29-45 | - |
| Cpt | H.T. Siegelmann | Welcoming the Super-Turing theories | Lecture Notes in Computer Science, Vol. 1012, eds. M. Bartosek, J. Staudek, J. Wiedermann, Springer Verlag | 1995 | 83-94 | - |
| Cpt | H.T. Siegelmann, B.G. Horne, and C.L. Giles | What NARX Networks Can Compute | Lecture Notes in Computer Science: Theory and Practice of Informatics, Vol. 1012, eds. M. Bartosek, J. Staudek, J. Wiedermann, Springer Verlag | 1995 | 95-102 | - |
| Cpt | B. DasGupta, H.T. Siegelmann, and E. Sontag | On the Intractability of Loading Neural Networks | Theoretical Advances in Neural Computation and Learning, eds. V.P. Roychowdhury, K.Y. Siu, and A. Orlitsky, Kluwer Academic Publishers | 1994 | - | - |
| Cpt | H.T. Siegelmann | On the Computational Power of Probabilistic and Faulty Neural Networks | Lecture Notes in Computer Science, Vol. 820: Automata, Languages and Programming, eds. S. Abiteboul and E. Shamir, Springer Verlag | 1994 | 20-34 | - |
| Cpt | H.T. Siegelmann and O. Frieder | Document Allocation in Multiprocessor Information Retrieval Systems | Lecture Notes in Computer Science, Vol. 759: Advanced Database Concepts and Research Issues, eds. N.R. Adam and B. Bhargava, Springer Verlag | 1993 | 289-310 | - |
| Cnf | M. Olsen, N. Siegelmann-Danieli, H. Siegelmann | Computational Modeling Reveals the Crucial Role of Cellular Citizenship in Selective Tumor Apoptosis | CSB2 Systems Biology of Human Diseases | 2009 | - | - |
| Cnf | Y.Z. Levy, D. Levy, J.S. Meyer, H.T. Siegelmann | Drug Addiction: a computational multiscale model combining neuropsychology, cognition, and behavior | International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS 2009) | 2009 | - | - |
| Cnf | Y.Z. Levy, D. Levy, J.S. Meyer, H.T. Siegelmann | Drug Addiction as a Non-monotonic Process: a Multiscale Computational Model | 13th International Conference on Biomedical Engineering (ICBME 2008) | 2008 | - | - |
| Cnf | David G Cooper, Dov Katz, Hava T. Siegelmann | Emotional Robotics: Tug of War | AAAI Spring Symposium on Emotion, Personality and Social Behavior | 2008 | - | |
| Cnf | Megan M. Olsen, Kyle T. Harrington, Hava T. Siegelmann | Emotions for Strategic Real-Time Systems | AAAI Spring Symposium on Emotion, Personality and Social Behavior | 2008 | - | |
| Cnf | David G Cooper, Dov Katz, Hava T. Siegelmann | Emotional Robotics: Tug of War | New England Student Conference on Artificial Intelligence(NESCAI) | 2008 | - | |
| Cnf | Megan M. Olsen, Kyle Harrington, Hava T. Siegelmann | Utilizing Emotions in Strategic Real-Time Artificial Intelligence | New England Student Conference on Artificial Intelligence(NESCAI) | 2008 | - | |
| Cnf | Megan M. Olsen, Kyle Harrington, Hava T. Siegelmann | A Multi-agent System that Attains Longevity Via Death | New England Student Conference on Artificial Intelligence(NESCAI) | 2007 | - | |
| Cnf | M. Olsen and H. Siegelmann | Multi-Agent System that Attains Longevity via Death | Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI) | 2007 | - | |
| Cnf | L. Holzman and H. Siegelmann | Input-Driven Dynamic Attractors | Computational and Systems Neuroscience, Salt Lake City | 2007 | - | |
| Cnf | L. Holzman and H. Siegelmann | Exact Neural Inference over Graphical Models | Computational and Systems Neuroscience Salt Lake City | 2007 | - | |
| Cnf | M. Olsen and H. Siegelmann | Artificial Death for Attaining System Longevity | Proceedings of the 50th Anniversary Summit of Artificial Intelligence Summit, Switzerland | 2006 | 217-218 | |
| Cnf | Kyle Harrington and Hava Siegelmann | Adaptive Multi-Modal Sensors | Proceedings of the 50th Anniversary Summit of Artificial Intelligence Summit, Switzerland | 2006 | - | |
| Cnf | Will Bush and Hava Siegelmann | Genetic Clock Oscillator Neural Networks | Computational Neural systems, Salt Lake City | 2005 | - | abstract |
| Cnf | AnYuan Guo and Hava Siegelmann | Time-Warped Longest Common Subsequence Algorithm for Music Retrieval | International Conference on Music Information Retrieval (ISMIR'04) | 2004 | - | |
| Cnf | Eric Bittman, Yossi Chait, C.V. Hollot, M. Harrington and H. Siegelmann | Is the Mammalian Circadian Clock a Resonant-Circuit Oscillator? | Society for Research on Biological Rhythms, Whistler, BC | 2004 | - | - |
| Cnf | Yanhong Tong and Hava Sieglemann | Simulation mammalian molecular circadian oscillators by dynamic gene network | Eighth Annual International Conference on Research in Computational Molecular Biology | 2004 | - | - |
| Cnf | Shaolei Lu, AnYuan Guo, Klaus Becker, Hava Siegelmann, Paola Sebastiani, Kyle MacBeth, Joseph Jerry | Microarray Analysis of Global Gene Expression in the Mammary Gland Following Estrogen and Progesterone Treatment of Ovariectomized Mice, | Second Annual AACR International Conference on Frontiers in Cancer Prevention Research, Phoenix, Arizona | 2003 | - | - |
| Cnf | T. Jaakkola and H. Siegelmann | Active information retrieval | Advances in Neural Information Processing Systems 14 | 2001 | - | |
| Cnf | Pedro Rodrigues, Jose Felix Costa, Hava T. Siegelmann | Verifying Properties of Neural Networks | IWANN (1) | 2001 | 158-165 | |
| Cnf | A. Ben-Hur and H.T. Siegelmann | Computation in Gene Networks | MCU 2001, LNCS 2055, eds. M. Margenstern and Y. Rogozhin | 2001 | 11-24 | |
| Cnf | D. Horn, I. Opher, M. Epstein and H. T. Siegelmann | Clustering of Documents using Latent Semantic Analysis | Proceedings of the DAS2000, Rio | 2000 | - | - |
| Cnf | A. Ben-Hur, D. Horn, H.T. Siegelmann and V. Vapnik | A Support Vector Method for Hierarchical Clustering | Fourteenth Annual Conference on Neural Information Processing Systems, Denver, Colorado | 2000 | - | |
| Cnf | A. Ben-Hur, D. Horn, H.T. Siegelmann and V. Vapnik | A Support Vector Method for Clustering | Proceedings of the 15th International Conference on Pattern Recognition (ICPR) | 2000 | 728-731 | |
| Cnf | H.T. Siegelmann, A. Roitershtein, and A. Ben-Hur | Noisy Neural Networks and Generalizations | Proceedings of Thirteenth Annual Conference on Neural Information Processing Systems, Denver, Colorado | 1999 | - | |
| Cnf | H.T. Siegelmann, A. Ben-Hur, and S. Fishman | Computational Complexity for Continuous Time Dynamics | Proceedings of Third International Conference on Computing Anticipatory Systems (CASYS'99), Liege, Belgium | 1999 | - | - |
| Cnf | Hod Lipson, Hava T. Siegelmann | High Order Eigentensors as Symbolic Rules in Competitive Learning | Hybrid Neural Systems | 2000 | 286-297 | |
| Cnf | H.T. Siegelmann and S. Fishman | Attractor Systems and Analog Computation | Proceedings of the Second International Conference on Knowledge-Based Intelligent Electronic Systems (KES'98), Adelaide, Australia | 1998 | - | |
| Cnf | H. Lipson, Y. Hod, and H.T. Siegelmann | High-Order Clustering Metrics for Competitive Learning Neural Networks | Proceedings of the Israel-Korea Bi-National Conference on New Themes in Computer Aided Geometric Modeling, Tel-Aviv, Israel | 1998 | - | - |
| Cnf | D. Lange, G.F. Inbar, H. Pratt, and H.T. Siegelmann | Unsupervised Identification of Event-Related Brain Potentials via Competitive Learning | IEEE Engineering in Medicine and Biology Society, Vol 20, No 3 | 1998 | 1329-1332 | |
| Cnf | J.P. Neto, H.T. Siegelmann, and J.F. Costa | Turing Universality of Neural Nets Revisited | Proceedings of the Sixth International Conference on Computer Aided Systems Technology (EUROCAST'97). Lecture Notes in Computer Science (LNCS) 1333, eds. Franz Pichler and Roberto Moreno-Diaz | 1997 | 361-366 | |
| Cnf | D.H. Lange, H.T. Siegelmann, H. Pratt, and G.F. Inbar | A Generic Approach for Identification of Event Related Brain Potentials via a Competitive Neural Network Structure | Proceedings of the International Conference on Neural Information Proceeding (NIPS), Denver, Colorado | 1997 | - | |
| Cnf | Y. Finkelstein and H.T. Siegelmann | A Stochastic Model to Study Degenerative Disorders in the Central Nervous System | The Israel Neurological Association Annual Meeting, Zichron-Yaakov | 1997 | - | - |
| Cnf | H.T. Siegelmann and S. Fishman | Computation by Dynamical Systems | Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics | 1997 | - | |
| Cnf | H.T. Siegelmann, A. Ofri, and H. Guterman | Applying Modular Networks and Fuzzy Logic controllers to nonlinear Flexible Structures | Proceedings of the IEEE International Conference on Fuzzy Logic | 1997 | - | |
| Cnf | J.P. Neto, H.T. Siegelmann, and J.F. Costa | Implementation of Programming Languages with Neural Nets | Proceedings of the First International Conference on Computing Anticipatory Systems (CASYS'97), HEC, Liege, Belgium | 1997 | - | - |
| Cnf | G. Arieli and H.T. Siegelmann | ANN Approach vs. the Symbolic Approach in AI | Proceedings of the Thirteenth Israeli Conference on Artificial Intelligence and Computer Vision (IAICV'97), Tel-Aviv | 1997 | - | - |
| Cnf | H.T. Siegelmann | Recurrent Neural Networks | AMS Proceedings, Park-City, 8 | 1995 | - | - |
| Cnf | Hava T. Siegelmann | Welcoming the Super Turing Theories | SOFSEM | 1995 | - | - |
| Cnf | Bill G. Horne, Hava T. Siegelmann, C. Lee Giles | What NARX Networks Can Compute | SOFSEM | 1995 | 95-102 | - |
| Cnf | H.T. Siegelmann | Recurrent Neural Networks and Finite Automata | Proceedings of the Twelfth International Conference on Pattern Recognition | 1994 | - | - |
| Cnf | E. Nissan, H.T. Siegelmann, and A. Galperin | An Integrated Symbolic and Neural Network Architecture for Machine Learning in the domain of Nuclear Engineering | Proceedings of the Twelfth International Conference on Pattern Recognition | 1994 | - | |
| Cnf | E. Nissan, H.T. Siegelmann, A. Galperin, and S. Kimhi | Towards Full Atomization of the Discovery of Heuristics in a Nuclear Engineering Project: Integration with a Neural Information Language | Proceedings of the Eight International Symposium on Methodologies for Intelligent Systems | 1994 | - | - |
| Cnf | H.T. Siegelmann | Neural Programming Language | Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), Vol 2. AAAI Press/The MIT Press | 1994 | 877-882 | - |
| Cnf | B. DasGupta, H.T. Siegelmann, and E. Sontag | On a Learnability Question Associated to Neural Networks with Continuous Activations | Proceedings of the Sixth ACM Workshop on Computational Learning (COLT), New Brunswick NJ | 1994 | - | |
| Cnf | H.T. Siegelmann | On the Computational Power of Probabilistic and Faulty Neural Networks | Proceedings of the International Colloquium on Automata, Languages, and Programming (ICALP) | 1994 | - | - |
| Cnf | J. Kilian and H.T. Siegelmann | On the Power of Sigmoid Neural Networks | Proceedings of the Fifth ACM Workshop on Computational Learning (COLT), Santa Cruz | 1993 | 137-143 | |
| Cnf | Hava T. Siegelmann, Ophir Frieder | Document Allocation In Multiprocessor Information Retrieval Systems | Advanced Database Systems | 1993 | 289-310 | - |
| Cnf | H.T. Siegelmann and E.D. Sontag | Analog Computation via Neural Networks | Proceedings of the Second Israel Symposium on Theory of Computing and Systems (ISTCS), Natanya, Israel | 1993 | 98-107 | |
| Cnf | J.L. Balcázar, R. Gavalda, JH.T. Siegelmann, and E.D. Sontag | Some Structural Complexity Aspects of Neural Computation | Proceedings of the IEEE Conference on Structure in Complexity Theory, San Diego, California | 1993 | 253-265 | |
| Cnf | H.T. Siegelmann and E.D. Sontag | Some Recent Results on Computing with 'Neural Nets' | Proceedings of the IEEE Conference on Decision and Control, Tucson, Arizona | 1992 | 1476-1481 | |
| Cnf | H.T. Siegelmann and E.D. Sontag | On the Computational Power of Neural Nets | Proceedings of the Fifth ACM Workshop on Computational Learning Theory (COLT), Pittsburgh, Penn. | 1992 | 440-449 | |
| Cnf | H.T. Siegelmann, E.D. Sontag, and C.L. Giles | The Complexity of Language Recognition by Neural Networks | Algorithms, Software, Architecture, ed. J. van Leuwen, North Holland, Amsterdam (Proceedings of the Twelfth IFIP World Computer Congress) | 1992 | 329-335 | - |
| Cnf | H.T. Siegelmann and O. Frieder | The Allocation of Documents in Multiprocessor Information Retrieval Systems: An Application of Genetic Algorithms | Proceedings of the IEEE Conference on Systems, Man, and Cybernetics, Charlottesville, Virginia | 1991 | - | |
| Cnf | O. Frieder and H.T. Siegelmann | On the Allocation of Documents in Multiprocessor Information Retrieval Systems | Proceedings of the ACM Fourteenth Conference on Information Retrieval (SIGIR), Chicago, Illinois | 1991 | - | |
| Cnf | H.T. Siegelmann and B.R. Badrinath | Integrating Implicit Answers with Object-Oriented Queries | Proceedings of the Conference on Very Large Data Bases, Barcelona, Spain | 1991 | - |
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