English

Recurrent Memory Array Structures

Machine Learning 2016-10-25 v3 Neural and Evolutionary Computing

Abstract

The following report introduces ideas augmenting standard Long Short Term Memory (LSTM) architecture with multiple memory cells per hidden unit in order to improve its generalization capabilities. It considers both deterministic and stochastic variants of memory operation. It is shown that the nondeterministic Array-LSTM approach improves state-of-the-art performance on character level text prediction achieving 1.402 BPC on enwik8 dataset. Furthermore, this report estabilishes baseline neural-based results of 1.12 BPC and 1.19 BPC for enwik9 and enwik10 datasets respectively.

Keywords

Cite

@article{arxiv.1607.03085,
  title  = {Recurrent Memory Array Structures},
  author = {Kamil Rocki},
  journal= {arXiv preprint arXiv:1607.03085},
  year   = {2016}
}

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Minor changes

R2 v1 2026-06-22T14:51:34.878Z