We present a neural network employing Hebbian storage and sparse internal coding, which is capable of memorizing and correcting sequences of binary vectors ...
Neural networks appear to be natural devices for memorizing binary vector sequences. A version of the Kanerva memory [I], employing random, time-varying.
A ternary version of the Kanerva memory, folded into a feedback configuration, is shown to perform the basic sequence memorization and regeneration function ...
Baram. Memorizing binary vector sequences by a sparsely encoded network. IEEE Transactions on Neural Networks. (1994). G. Boffetta et al. Symmetry breaking in ...
We present a neural network employing Hebbian storage and sparse internal coding, which is capable of memorizing and correcting sequences of binary vectors by ...
We propose a systematic approach to reduce the memory consumption of deep neural network training. Specifically, we design an algorithm that costs O(sqrt(n)) ...
Missing: Memorizing | Show results with:Memorizing
We present a neural network employing Hebbian storage and sparse internal coding, which is capable of memorizing and correcting sequences of binary vectors by ...
The theory analyzes memory networks for two different types of input data, symbolic or analog. Symbolic inputs, encoded by one-hot and binary input vectors, ...
Baram, "Memorizing binary vector sequences by a sparsely encoded network," IEEE Trans. Neural Networks, vol. 5, pp. 974-981, 1994. [2] J. M. Barnes and ...
The Sparse Distributed Memory (SDM) proposed by Kanerva provides a simple model for human long-term memory, with a strong underlying mathematical theory.