File:arxiv 1412.3555 Fig1.png
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- (Chung et al., 2014) ⇒ Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. (2014). "Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling". In: Proceedings of the Deep Learning and Representation Learning Workshop at NIPS 2014.
- Figure 1: Illustration of (a) LSTM and (b) gated recurrent units. (a) [math]\displaystyle{ i }[/math], [math]\displaystyle{ f }[/math] and [math]\displaystyle{ o }[/math] are the input, forget and output gates, respectively. [math]\displaystyle{ c }[/math] and [math]\displaystyle{ \tilde{c} }[/math] denote the memory cell and the new memory cell content. (b) [math]\displaystyle{ r }[/math] and [math]\displaystyle{ z }[/math] are the reset and update gates, and [math]\displaystyle{ h }[/math] and [math]\displaystyle{ \tilde{h} }[/math] are the activation and the candidate activation
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| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 21:01, 8 July 2018 | | 820 × 337 (40 KB) | Omoreira (talk | contribs) | * Chung et al., 2014) ⇒ Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. (2014). [https://arxiv.org/pdf/1412.3555.pdf "Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling"]. In: Proce... |
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