# Diagonal RNNs in Symbolic Music Modeling

arXiv: https://arxiv.org/abs/1704.05420 · HTML: https://mlsanity.com/paper/1704.05420/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2017.

## Built on by (most cited first, 2 of 2)

- [Adaptive Slimming for Scalable and Efficient Speech Enhancement](https://mlsanity.com/paper/2507.04879/) (2025)
- [Gates Are Not What You Need in RNNs](https://mlsanity.com/paper/2108.00527/) (2021)

## Abstract (arXiv)

In this paper, we propose a new Recurrent Neural Network (RNN) architecture. The novelty is simple: We use diagonal recurrent matrices instead of full. This results in better test likelihood and faster convergence compared to regular full RNNs in most of our experiments. We show the benefits of using diagonal recurrent matrices with popularly used LSTM and GRU architectures as well as with the vanilla RNN architecture, on four standard symbolic music datasets.