Computational complexity of neural networks: a survey
暂无分享,去创建一个
[1] William H. Kautz,et al. On the Size of Weights Required for Linear-Input Switching Functions , 1961, IRE Transactions on Electronic Computers.
[2] S. Muroga,et al. Theory of majority decision elements , 1961 .
[3] Marvin Minsky,et al. Perceptrons: An Introduction to Computational Geometry , 1969 .
[4] Saburo Muroga,et al. Threshold logic and its applications , 1971 .
[5] Kaoru Nakano,et al. Associatron-A Model of Associative Memory , 1972, IEEE Trans. Syst. Man Cybern..
[6] Teuvo Kohonen,et al. Correlation Matrix Memories , 1972, IEEE Transactions on Computers.
[7] Teuvo Kohonen,et al. Representation of Associated Data by Matrix Operators , 1973, IEEE Transactions on Computers.
[8] Richard J. Lipton,et al. Some connections between nonuniform and uniform complexity classes , 1980, STOC '80.
[9] Eric Goles Ch.,et al. The Convergence of Symmetric Threshold Automata , 1981, Inf. Control..
[10] J J Hopfield,et al. Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.
[11] Eric Goles Ch.,et al. Transient length in sequential iteration of threshold functions , 1983, Discret. Appl. Math..
[12] Andrew Chi-Chih Yao,et al. Separating the Polynomial-Time Hierarchy by Oracles (Preliminary Version) , 1985, FOCS.
[13] Eric Goles Ch.,et al. Decreasing energy functions as a tool for studying threshold networks , 1985, Discret. Appl. Math..
[14] Noga Alon. Asynchronous threshold networks , 1985, Graphs Comb..
[15] Sompolinsky,et al. Storing infinite numbers of patterns in a spin-glass model of neural networks. , 1985, Physical review letters.
[16] Johan Håstad,et al. Almost optimal lower bounds for small depth circuits , 1986, STOC '86.
[17] L. Personnaz,et al. Collective computational properties of neural networks: New learning mechanisms. , 1986, Physical review. A, General physics.
[18] James L. McClelland,et al. Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .
[19] Ingo Wegener,et al. The complexity of Boolean functions , 1987 .
[20] Yves Robert,et al. Automata networks in computer science : theory and applications , 1987 .
[21] José L. Balcázar,et al. On Characterizations of the Class PSPACE/POLY , 1987, Theor. Comput. Sci..
[22] Santosh S. Venkatesh,et al. The capacity of the Hopfield associative memory , 1987, IEEE Trans. Inf. Theory.
[23] Michael Luby,et al. Steepest Descent Can Take Exponential Time for Symmetric Connection Networks , 1988, Complex Syst..
[24] James A. Anderson,et al. Neurocomputing: Foundations of Research , 1988 .
[25] D. O. Hebb,et al. The organization of behavior , 1988 .
[26] Jia-Wei Hong. On connectionist models , 1988 .
[27] P. Raghavan,et al. Learning in threshold networks , 1988, COLT '88.
[28] J. Stephen Judd,et al. On the complexity of loading shallow neural networks , 1988, J. Complex..
[29] Teuvo Kohonen,et al. Self-organization and associative memory: 3rd edition , 1989 .
[30] M. Kearns,et al. Crytographic limitations on learning Boolean formulae and finite automata , 1989, STOC '89.
[31] Demetri Psaltis,et al. Linear and logarithmic capacities in associative neural networks , 1989, IEEE Trans. Inf. Theory.
[32] Pekka Orponen,et al. On the Computational Complexity of Analyzing Hopfield Nets , 1989, Complex Syst..
[33] Ken-ichi Funahashi,et al. On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.
[34] Anthony Kuh,et al. Information capacity of associative memories , 1989, IEEE Trans. Inf. Theory.
[35] Eric Goles Ch.,et al. Exponential Transient Classes of Symmetric Neural Networks for Synchronous and Sequential Updating , 1989, Complex Syst..
[36] Amir Dembo,et al. On the capacity of associative memories with linear threshold functions , 1989, IEEE Trans. Inf. Theory.
[37] S. Franklin,et al. Neural computability. II , 1989, International 1989 Joint Conference on Neural Networks.
[38] Georg Schnitger,et al. Relating Boltzmann machines to conventional models of computation , 1987, Neural Networks.
[39] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[40] Zoran Obradovic,et al. Analog Neural Networks of Limited Precision I: Computing with Multilinear Threshold Functions , 1989, NIPS.
[41] Martin Hasler,et al. Recursive neural networks for associative memory , 1990, Wiley-interscience series in systems and optimization.
[42] W S McCulloch,et al. A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.
[43] Jehoshua Bruck,et al. Neural computation of arithmetic functions , 1990 .
[44] Emile H. L. Aarts,et al. Simulated annealing and Boltzmann machines - a stochastic approach to combinatorial optimization and neural computing , 1990, Wiley-Interscience series in discrete mathematics and optimization.
[45] Jirí Wiedermann,et al. Complexity Issues in Discrete Neurocomputing , 1990, IMYCS.
[46] Jehoshua Bruck. On the convergence properties of the Hopfield model , 1990, Proc. IEEE.
[47] Max H. Garzon,et al. Global Dynamics in Neural Networks II , 1990, Complex Syst..
[48] J. Stephen Judd,et al. Neural network design and the complexity of learning , 1990, Neural network modeling and connectionism.
[49] J.A. Anderson,et al. Directions for research , 1990 .
[50] Noga Alon,et al. Efficient simulation of finite automata by neural nets , 1991, JACM.
[51] Mihalis Yannakakis,et al. Simple Local Search Problems That are Hard to Solve , 1991, SIAM J. Comput..
[52] Alon Orlitsky,et al. A geometric approach to threshold circuit complexity , 1991, COLT '91.
[53] Georg Schnitger,et al. On the computational power of sigmoid versus Boolean threshold circuits , 1991, [1991] Proceedings 32nd Annual Symposium of Foundations of Computer Science.
[54] Jehoshua Bruck,et al. On the Power of Threshold Circuits with Small Weights , 1991, SIAM J. Discret. Math..
[55] Hava T. Siegelmann,et al. On the computational power of neural nets , 1992, COLT '92.
[56] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..
[57] Kai-Yeung Siu,et al. Optimal Depth Neural Networks for Multiplication and Related Problems , 1992, NIPS.
[58] Ronald L. Rivest,et al. Training a 3-node neural network is NP-complete , 1988, COLT '88.
[59] J. Reif,et al. On Threshold Circuits and Polynomial Computation , 1992, SIAM J. Comput..
[60] Georg Schnitger,et al. The Power of Approximation: A Comparison of Activation Functions , 1992, NIPS.
[61] Eduardo D. Sontag,et al. Feedforward Nets for Interpolation and Classification , 1992, J. Comput. Syst. Sci..
[62] Marek Karpinski,et al. Simulating threshold circuits by majority circuits , 1993, SIAM J. Comput..
[63] Alexander A. Razborov,et al. n^Omega(log n) Lower Bounds on the Size of Depth-3 Threshold Circuits with AND Gates at the Bottom , 1993, Information Processing Letters.
[64] Pavel Pudlák,et al. Threshold circuits of bounded depth , 1987, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987).
[65] Pekka Orponen,et al. On the Computational Power of Discrete Hopfield Nets , 1993, ICALP.
[66] Wolfgang Maass,et al. Bounds for the computational power and learning complexity of analog neural nets , 1993, SIAM J. Comput..
[67] Pekka Orponen,et al. Attraction Radii in Binary Hopfield Nets are Hard to Compute , 1993, Neural Computation.
[68] Ian Parberry,et al. Circuit complexity and neural networks , 1994 .
[69] R. Palmer,et al. Introduction to the theory of neural computation , 1994, The advanced book program.
[70] Johan Håstad,et al. On the Size of Weights for Threshold Gates , 1994, SIAM J. Discret. Math..
[71] José L. Balcázar,et al. Structural Complexity I , 1995, Texts in Theoretical Computer Science An EATCS Series.