Tuning capability of binary perceptron

Handwritten Digit Recognition with Binary Optical Perceptron Handwritten Digit Recognition with Binary Optical Perceptron

Binary weights are favored in electronic and optical hardware implementations of neural networks as they lead to improved system speeds. Optical neural networks based on fast ferroelectric liquid crystal binary level devices can beneet from the many orders of magnitudes improved liquid crystal response times. An optimized learning algorithm for all-positive perceptrons is simulated on a limited...

Landscape statistics of the binary perceptron

2014 The landscape of the binary perceptron is studied by Simulated Annealing, exhaustive search and performing random walks on the landscape. We find that the number of local minima increases exponentially with the number of bonds, becoming deeper in the vicinity of a global minimum, but more and more shallow as we move away from it. The random walker detects a simple dependence on the size of...

Entropy landscape of solutions in the binary perceptron problem

Abstract. The statistical picture of the solution space for a binary perceptron is studied. The binary perceptron learns a random classification of input random patterns by a set of binary synaptic weights. The learning of this network is difficult especially when the pattern (constraint) density is close to the capacity, which is supposed to be intimately related to the structure of the soluti...

Application of Potts-Model Perceptron for Binary Patterns Identification

We suggest an effective algorithm based on q−state Potts model providing an exponential growth of network storage capacity M ∼ N, where N is the dimension of the binary patterns and S is the free parameter of task. The algorithm allows us to identify a large number of highly distorted similar patterns. The negative influence of correlations of the patterns is suppressed by choosing a sufficient...

Storage capacity of the reversed wedge perceptron with binary connections