# A Clockwork RNN

arXiv: https://arxiv.org/abs/1402.3511 · HTML: https://mlsanity.com/paper/1402.3511/

## Key facts

- Cited by 72 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2014.

## Built on by (most cited first, 30 of 72)

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- [Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning](https://mlsanity.com/paper/2205.14794/) (2022)
- [Hierarchical Recurrent Neural Networks for Conditional Melody Generation with Long-term Structure](https://mlsanity.com/paper/2102.09794/) (2021)
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- [Multi-Horizon Representations with Hierarchical Forward Models for Reinforcement Learning](https://mlsanity.com/paper/2206.11396/) (2022)
- [SutraNets: Sub-series Autoregressive Networks for Long-Sequence, Probabilistic Forecasting](https://mlsanity.com/paper/2312.14880/) (2023)
- [Integration of Mamba and Transformer -- MAT for Long-Short Range Time Series Forecasting with Application to Weather Dynamics](https://mlsanity.com/paper/2409.08530/) (2024)
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- [RotRNN: Modelling Long Sequences with Rotations](https://mlsanity.com/paper/2407.07239/) (2024)
- [Deep Probabilistic Koopman: Long-term time-series forecasting under periodic uncertainties](https://mlsanity.com/paper/2106.06033/) (2021)
- [Recurrent Neural Networks for Learning Long-term Temporal Dependencies with Reanalysis of Time Scale Representation](https://mlsanity.com/paper/2111.03282/) (2021)
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- [Recurrent Neural Networks with Mixed Hierarchical Structures for Natural Language Processing](https://mlsanity.com/paper/2106.02562/) (2021)

## Abstract (arXiv)

Sequence prediction and classification are ubiquitous and challenging problems in machine learning that can require identifying complex dependencies between temporally distant inputs. Recurrent Neural Networks (RNNs) have the ability, in theory, to cope with these temporal dependencies by virtue of the short-term memory implemented by their recurrent (feedback) connections. However, in practice they are difficult to train successfully when the long-term memory is required. This paper introduces a simple, yet powerful modification to the standard RNN architecture, the Clockwork RNN (CW-RNN), in which the hidden layer is partitioned into separate modules, each processing inputs at its own temporal granularity, making computations only at its prescribed clock rate. Rather than making the standard RNN models more complex, CW-RNN reduces the number of RNN parameters, improves the performance significantly in the tasks tested, and speeds up the network evaluation. The network is demonstrated in preliminary experiments involving two tasks: audio signal generation and TIMIT spoken word classification, where it outperforms both RNN and LSTM networks.