- tags
- Recurrent neural networks
- source
- (Voelker et al., 2019)

## Summary

This paper introduces the LMU recurrent cell. This cell is based on a similar-ish idea from LSTM to maintain a memory hidden state. The main idea of the paper is to make this memory satisfy a set of first order ordinary differential equations. \begin{equation} θ \dot{m}(t) = Am(t) + Bu(t) \end{equation} This system has a solution which represents sliding windows of \(u\) via Legendre polynomials. This new unit is tested on a range of tasks. A memory only task, a permuted MNIST task and a dynamical chaotic system prediction task.

## Comments

Unfortunately, my understanding of this paper is slightly limited. The approach is interesting and has good properties, however the new RNN cell is tested on a small set of task that independently demonstrate useful properties but not all of them together (e.g. good MNIST prediction + long-term dependency).

# Bibliography

Voelker,
A., Kaji'c, Ivana, & Eliasmith, C., *Legendre Memory Units:
Continuous-Time Representation in Recurrent Neural Networks*,
In H. Wallach, H. Larochelle, A. Beygelzimer, F.
d{\textbackslash}textquotesingle {Alch{'e}-Buc}, E. Fox, & R.
Garnett (Eds.), Advances in {{Neural Information Processing
Systems}} 32 (pp. 15544–15553) (2019). : {Curran Associates,
Inc.}. ↩