mlsanityData to 7 Oct 2026

RotLSTM: Rotating Memories in Recurrent Neural Networks

arXiv 2105.00357 ↗First posted 1 May 2021cs.LGcited by 1

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Abstract (arXiv)

Long Short-Term Memory (LSTM) units have the ability to memorise and use long-term dependencies between inputs to generate predictions on time series data. We introduce the concept of modifying the cell state (memory) of LSTMs using rotation matrices parametrised by a new set of trainable weights. This addition shows significant increases of performance on some of the tasks from the bAbI dataset.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 21 references that are in the map. First posted on arXiv 1 May 2021.

Builds on

17 of 21 references.

References · 17

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5 more
  1. 2016Programming with a Differentiable Forth Interpretercited by 33
  2. 2017Gated Orthogonal Recurrent Units: On Learning to Forgetcited by 31
  3. 2016Sequence-to-sequence neural network models for transliterationcited by 7
  4. 2017SCAN: Learning Hierarchical Compositional Visual Conceptscited by 6
  5. 2017Rotational Unit of Memorycited by 1

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024RotRNN: Modelling Long Sequences with Rotationscited by 6