A memory enhanced LSTM for modeling complex temporal dependencies

Sneha Aenugu

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arXiv · 1910.12388

A memory enhanced LSTM for modeling complex temporal dependencies

Sneha Aenugu

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Abstract

In this paper, we present Gamma-LSTM, an enhanced long short term memory (LSTM) unit, to enable learning of hierarchical representations through multiple stages of temporal abstractions. Gamma memory, a hierarchical memory unit, forms the central memory of Gamma-LSTM with gates to regulate the information flow into various levels of hierarchy, thus providing the unit with a control to pick the appropriate level of hierarchy to process the input at a given instant of time. We demonstrate better performance of Gamma-LSTM model regular and stacked LSTMs in two settings (pixel-by-pixel MNIST digit classification and natural language inference) placing emphasis on the ability to generalize over long sequences.

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Sneha Aenugu. 2019-10-25. A memory enhanced LSTM for modeling complex temporal dependencies. https://arxiv.org/abs/1910.12388

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