English

LSTM with Working Memory

Neural and Evolutionary Computing 2017-04-03 v3

Abstract

Previous RNN architectures have largely been superseded by LSTM, or "Long Short-Term Memory". Since its introduction, there have been many variations on this simple design. However, it is still widely used and we are not aware of a gated-RNN architecture that outperforms LSTM in a broad sense while still being as simple and efficient. In this paper we propose a modified LSTM-like architecture. Our architecture is still simple and achieves better performance on the tasks that we tested on. We also introduce a new RNN performance benchmark that uses the handwritten digits and stresses several important network capabilities.

Keywords

Cite

@article{arxiv.1605.01988,
  title  = {LSTM with Working Memory},
  author = {Andrew Pulver and Siwei Lyu},
  journal= {arXiv preprint arXiv:1605.01988},
  year   = {2017}
}

Comments

Accepted at IJCNN 2017

R2 v1 2026-06-22T13:54:55.482Z