arXiv (Cornell University)· 2014· Preprint
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- 10,809citations
- 2014year
Short summary
Gated recurrent units (GRUs) and Long Short-Term Memory (LSTM) networks significantly outperform traditional recurrent units like tanh for sequence modeling tasks, with GRUs showing comparable performance to LSTMs.
AI-generated from the title and abstract; the full text is not read.
Key points
- Gated recurrent units (GRU and LSTM) outperform traditional tanh units in RNNs for sequence modeling.
- GRU and LSTM were evaluated on polyphonic music and speech signal modeling tasks.
- GRU performance was found to be comparable to LSTM performance.
AI-generated from the title and abstract; the full text is not read.
Abstract
In this paper we compare different types of recurrent units in recurrent neural networks (RNNs). Especially, we focus on more sophisticated units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU). We evaluate these recurrent units on the tasks of polyphonic music modeling and speech signal modeling. Our experiments revealed that these advanced recurrent units are indeed better than more traditional recurrent units such as tanh units. Also, we found GRU to be comparable to LSTM.
The authors' abstract, as published at the source. arXiv (Cornell University), 2014 · DOI ↗
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Field: Signal Processing
Signal ProcessingComputer Science