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Article cité :
Werner Krauth , Marc Mézard
J. Phys. France, 50 20 (1989) 3057-3066
Citations de cet article :
231 articles | Pages :
Stability of the one-step replica-symmetry-broken phase in neural networks
E A Dorotheyev Journal of Physics A: Mathematical and General 25 (21) 5527 (1992) https://doi.org/10.1088/0305-4470/25/21/012
Learning unlearnable problems with perceptrons
Timothy L. H. Watkin and Albrecht Rau Physical Review A 45 (6) 4102 (1992) https://doi.org/10.1103/PhysRevA.45.4102
Study of a learning algorithm for neural networks with discrete synaptic couplings
C J Pérez Vicente, J Carrabina and E Valderrama Network: Computation in Neural Systems 3 (2) 165 (1992) https://doi.org/10.1088/0954-898X_3_2_005
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On the capacity of neural networks with binary weights
I Kocher and R Monasson Journal of Physics A: Mathematical and General 25 (2) 367 (1992) https://doi.org/10.1088/0305-4470/25/2/017
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Learning from examples in weight-constrained neural networks
R Meir and J F Fontanari Journal of Physics A: Mathematical and General 25 (5) 1149 (1992) https://doi.org/10.1088/0305-4470/25/5/021
Broken symmetries in multilayered perceptrons
E. Barkai, D. Hansel and H. Sompolinsky Physical Review A 45 (6) 4146 (1992) https://doi.org/10.1103/PhysRevA.45.4146
Statistical mechanics of learning from examples
H. S. Seung, H. Sompolinsky and N. Tishby Physical Review A 45 (8) 6056 (1992) https://doi.org/10.1103/PhysRevA.45.6056
Replica-symmetry breaking in neural networks
V.S. Dotsenko and B. Tirozzi Physica A: Statistical Mechanics and its Applications 185 (1-4) 385 (1992) https://doi.org/10.1016/0378-4371(92)90479-A
Study of a learning algorithm for neural networks with discrete synaptic couplings
C Vicente, J Carrabina and E Valderrama Network: Computation in Neural Systems 3 (2) 165 (1992) https://doi.org/10.1088/0954-898X/3/2/005
Selecting examples for perceptrons
T L H Watkin and A Rau Journal of Physics A: Mathematical and General 25 (1) 113 (1992) https://doi.org/10.1088/0305-4470/25/1/016
Hidden information in Hamiltonian systems with discrete weights
Ido Kanter Physical Review A 45 (8) 6051 (1992) https://doi.org/10.1103/PhysRevA.45.6051
Finite-state neural networks. A step toward the simulation of very large systems
G. A. Kohring Journal of Statistical Physics 62 (3-4) 563 (1991) https://doi.org/10.1007/BF01017973
Artificial Neural Networks
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Replica symmetry breaking in neural networks with modified pseudo-inverse interactions
V S Dotsenko and B Tirozzi Journal of Physics A: Mathematical and General 24 (21) 5163 (1991) https://doi.org/10.1088/0305-4470/24/21/026
Associative memory: on the (puzzling) sparse coding limit
Jean-P Nadal Journal of Physics A: Mathematical and General 24 (5) 1093 (1991) https://doi.org/10.1088/0305-4470/24/5/023
COLT
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Artificial Neural Networks
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Storage Capacity of a Multilayer Neural Network with Binary Weights
E Barkai and I Kanter Europhysics Letters (EPL) 14 (2) 107 (1991) https://doi.org/10.1209/0295-5075/14/2/003
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Finite-size effects and bounds for perceptron models
B Derrida, R B Griffiths and A Prugel-Bennett Journal of Physics A: Mathematical and General 24 (20) 4907 (1991) https://doi.org/10.1088/0305-4470/24/20/022
Learning from examples in large neural networks
H. Sompolinsky, N. Tishby and H. S. Seung Physical Review Letters 65 (13) 1683 (1990) https://doi.org/10.1103/PhysRevLett.65.1683
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On the capacity per synapse
I Kanter and E Eisenstein Journal of Physics A: Mathematical and General 23 (17) L935 (1990) https://doi.org/10.1088/0305-4470/23/17/016
Capacity of neural networks with discrete synaptic couplings
H Gutfreund and Y Stein Journal of Physics A: Mathematical and General 23 (12) 2613 (1990) https://doi.org/10.1088/0305-4470/23/12/036
Storage capacity of a diluted neural network with Ising couplings
M Bouten, A Komoda and R Serneels Journal of Physics A: Mathematical and General 23 (12) 2605 (1990) https://doi.org/10.1088/0305-4470/23/12/035
On the storage capacity with sign-constrained synaptic couplings
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Adaptive genetic algorithm for the binary perceptron problem
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Statistical Mechanics of Neural Networks
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First-order transition to perfect generalization in a neural network with binary synapses
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