Year: 2016
Type: preprint
Abstract: We introduce a general and simple structural design called Multiplicative Integration (MI) to improve recurrent neural networks (RNNs). MI changes the way in which information from difference sources flows and is integrated in the computational building block of an RNN, while introducing almost no extra parameters. The new structure can be easily embedded into many popular RNN models, including LSTMs and GRUs. We empirically analyze its learning behaviour and conduct evaluations on several tasks using different RNN models. Our experimental results demonstrate that Multiplicative Integration can provide a substantial performance boost over many of the existing RNN models.
Source: arXiv (Cornell University)
Authors: Wu, Yuhuai, Zhang, Saizheng, Zhang, Ying, Bengio, Yoshua, Salakhutdinov, Ruslan
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Award: G8876996369
OpenAlex ID: W2469894155