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Cited by 31Neural Networks and ApplicationsComplexity and Algorithms in GraphsDNA and Biological Computing

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A Comparison of the Computational Power of Sigmoid and Boolean Threshold Circuits

Maass, W., Schnitger, G., Sontag, E. D.·1994·Theoretical Advances in Neural Computation and Learning
DOI 10.1007/978-1-4615-2696-4_4
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Citations

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Research works printed but not linked: 14

  1. W.J. Bultman, “Topics in the Theory of Machine Learning and Neural Computing”, Ph.D Dissertation, University of Illinois at Chicago, 1991.
  2. B. DasGupta and G. Schnitger, “The power of Approximating: a Comparison of Activation Functions”, in Advances in Neural Information Processing Systems 5, S.E. Hanson, J.D. Cowan and C.L. Giles eds, pp. 615–622, 1993.
  3. J. Hertz, A. Krogh and R.G. Palmer, “Introduction to the Theory of Neural Computation”, Addison-Wesley, Redwood City, 1991.
  4. A. Hajnal, W. Maass, P. Pudlak, M. Szegedy and G. Turan, “Threshold Circuits of Bounded Depth”, Proc. of the 28th Annual Symp. on Foundations of Computer Science, pp. 99–110, 1987. To appear in J. Comp. Syst. Sci.. · DOI
  5. C.C. Klimasauskas, “The 1989 Neuro-Computing Bibliography”, MIT Press, Cambridge, 1989.
  6. W. Maass, “Bounds for the computational power and learning complexity of analog neural nets”, Proc. of the 25th ACM Symposium on the Theory of Computing, 1993, 335–344. · DOI
  7. W. Maass, G. Schnitger, E. D. Sontag, “On the computational power of sigmoid versus boolean threshold circuits”, Proc. of the 32nd Annual IEEE Symp. on Foundations of Computer Science, 1991, 767–776 · DOI
  8. W. Maass and G. Turan, “How Fast Can a Threshold Gate Learn?”, in: Computational Learning Theory and Natural Learning Systems: Constraints and Prospects, G. Drastal, S.J. Hanson and R. Rivest eds., MIT Press, to appear.
  9. S. Muroga, “Threshold Logic and its Applications”, Wiley, New York, 1971.
  10. E.L. Schwartz, “Computational Neuroscience”, MIT Press, Cambridge, 1990.
  11. E.D. Sontag, “Feedforward Nets for Interpolation and Classification”, J. Comp. Syst. Sci., 45, 1992, 20–48. · DOI
  12. E.D. Sontag and H.J. Sussmann, “Backpropagation can give rise to spurious local minima even for networks without hidden layers”, Complex Systems 3, pp. 91–106, 1989.
  13. E.D. Sontag and H.J. Sussmann, “Backpropagation separates where Perceptrons do”, Neural Networks, 4, pp. 243–249, 1991. · DOI
  14. S.M. Weiss, C.A. Kulikowsky, “Computer Systems that Learn”, Morgan Kaufmann, San Mateo, 1991.

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Authors
Maass, W., Schnitger, G., & Sontag, E. D.
Affiliations
  • Graz University of Technology (AT)
  • Paderborn University (DE)
  • Rutgers, The State University of New Jersey (US)
ORCID
Venue
Theoretical Advances in Neural Computation and Learning
Publisher
Springer US
Published
1994
Pages
127-151
DOI
10.1007/978-1-4615-2696-4_4
OpenAlex
W1484418787
Cited by
31
References
0
Citations by year
2026: 1 · 2021: 1 · 2020: 1 · 2019: 5 · 2018: 2 · 2017: 1 · 2016: 2 · 2015: 3 · 2013: 3
FWCI
1.33
Type
book chapter
Language
en
Open access
closed
Locations
Springer eBooks (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Complexity and Algorithms in Graphs
  • Physical Sciences › Mathematics › Theoretical Computer Science › DNA and Biological Computing
Keywords
computational power, artificial neural networks, massively parallel computing, threshold functions

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