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Hierarchical Recurrent Encoder-Decoder (HRED) Neural Network Training Algorithm: Difference between revisions
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Hierarchical Recurrent Encoder-Decoder (HRED) Neural Network Training Algorithm (view source)
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=== 2015 === | === 2015 === | ||
* ([[2015_AHierarchicalRecurrentEncoderDe|Sordoni et al., 2015]]) ⇒ [[Alessandro Sordoni]], [[Yoshua Bengio]], [[Hossein Vahabi]], [[Christina Lioma]], [[Jakob Grue Simonsen]], and [[Jian-Yun Nie]]. ([[2015]]). “[https://arxiv.org/pdf/1507.02221.pdf A Hierarchical Recurrent Encoder-Decoder for Generative Context-Aware Query Suggestion].” In: [[Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM 2015)]]. [https://doi.org/10.1145/2806416.2806493 DOI:10.1145/2806416.2806493]. [http://arxiv.org/abs/1507.02221 arXiv:1507.02221]. | * ([[2015_AHierarchicalRecurrentEncoderDe|Sordoni et al., 2015]]) ⇒ [[Alessandro Sordoni]], [[Yoshua Bengio]], [[Hossein Vahabi]], [[Christina Lioma]], [[Jakob Grue Simonsen]], and [[Jian-Yun Nie]]. ([[2015]]). “[https://arxiv.org/pdf/1507.02221.pdf A Hierarchical Recurrent Encoder-Decoder for Generative Context-Aware Query Suggestion].” In: [[Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM 2015)]]. [https://doi.org/10.1145/2806416.2806493 DOI:10.1145/2806416.2806493]. [http://arxiv.org/abs/1507.02221 arXiv:1507.02221]. | ||
** QUOTE: Our [[hierarchical recurrent encoder-decoder (HRED)]] is pictured in [[#FIG3|Figure 3]]. </s> Given a [[query]] in the [[session]], the [[model]] [[encode]]s the [[information]] seen up to that [[position]] and tries to [[predict]] the following [[query]]. </s> The [[process]] is iterated throughout all the [[queri]]es in the session. </s> In the [[forward pass]], the [[model]] [[compute]]s the [[query-level encoding]]s, the [[session-level recurrent state]]s and the [[log-likelihood]] of each [[query]] in the session given the previous ones. </s> In the [[backward pass]], the [[gradient]]s are computed and the [[parameter]]s are [[updated]]. </s> <P> | ** QUOTE: Our [[hierarchical recurrent encoder-decoder (HRED)]] is pictured in [[#FIG3|Figure 3]]. </s> Given a [[query]] in the [[session]], the [[model]] [[encode]]s the [[information]] seen up to that [[position]] and tries to [[predict]] the following [[query]]. </s> The [[process]] is iterated throughout all the [[queri]]es in the session. </s> In the [[forward pass]], the [[model]] [[compute]]s the [[query-level encoding]]s, the [[session-level recurrent state]]s and the [[log-likelihood]] of each [[query]] in the session given the previous ones. </s> In the [[backward pass]], the [[gradient]]s are computed and the [[parameter]]s are [[updated]]. </s> <P> <div id="FIG3"> | ||
<div id="FIG3"> | |||
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