LSTM-based Encoder-Decoder Network: Difference between revisions

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An [[LSTM-based Encoder-Decoder Network]] is an [[RNN-based encoder-decoder model]] composed of [[LSTM model]]s (an [[LSTM encoder]] and an [[LSTM decoder]]).
An [[LSTM-based Encoder-Decoder Network]] is an [[RNN/RNN-based encoder-decoder model]] composed of [[LSTM model]]s (an [[LSTM encoder]] and an [[LSTM decoder]]).
* <B>Context:</B>
* <B>Context:</B>
** It can be trained by a [[LSTM-based Encoder/Decoder RNN Training System]].
** It can be trained by a [[LSTM-based Encoder/Decoder RNN Training System]].
* <B>Example(s):</B>
* <B>Example(s):</B>
** a [[LSTM-based Encoder-Decoder Machine Translation Model]].
** an [[LSTM-based Encoder-Decoder Machine Translation Model]].
** a [[LSTM-based Encoder-Decoder Text Error Correction Model]].
** an [[LSTM-based Encoder-Decoder Text Error Correction Model]], such as an [[LSTM-based Encoder-Decoder WikiText Error Correction Model]].
** an [[LSTM+Attention-based Encoder-Decoder Model]].
** an [[LSTM+Attention-based Encoder-Decoder Model]].
** ...
**
* <B>Counter-Example(s):</B>
* <B>Counter-Example(s):</B>
** a [[GRU-based Encoder-Decoder RNN]].
** a [[GRU-based Encoder-Decoder RNN]].
* <B>See:</B> [[Neural seq2seq]], [[Bidirectional LSTM]].
* <B>See:</B> [[Neural seq2seq]], [[Bidirectional LSTM]].
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=== 2017 ===
=== 2017 ===
* ([[Robertson, 2017]]) ⇒ [[Sean Robertson]]. ([[2017]]). “[https://tensorflow.org/versions/r1.1/tutorials/seq2seq Translation with a Sequence to Sequence Network and Attention].” In: TensorFlow Tutorials
* ([[Robertson, 2017]]) ⇒ [[Sean Robertson]]. ([[2017]]). “[https://tensorflow.org/versions/r1.1/tutorials/seq2seq Translation with a Sequence to Sequence Network and Attention].” In: TensorFlow Tutorials
** QUOTE: A basic [[sequence-to-sequence model]], as introduced in [[Cho et al., 2014]] , consists of two recurrent neural networks (RNNs): an encoder that processes the input and a decoder that generates the output. This basic architecture is depicted below. <P> <HTML><CENTER><IMG WIDTH=600 SRC=http://tensorflow.org/versions/r1.1/images/basic_seq2seq.png></CENTER></HTML> <P>  Each box in the picture above represents a cell of the RNN, most commonly a [[GRU cell]] or an [[LSTM cell]] (see the RNN Tutorial for an explanation of those). Encoder and decoder can share weights or, as is more common, use a different set of parameters. Multi-layer cells have been successfully used in [[sequence-to-sequence model]]s too, e.g. for translation [[Sutskever et al., 2014]] .
** QUOTE: A basic [[sequence-to-sequence model]], as introduced in [[Cho et al., 2014]] , consists of two recurrent neural networks (RNNs): an encoder that processes the input and a decoder that generates the output. This basic architecture is depicted below. ...        <P>       ... Each box in the picture above represents a cell of the RNN, most commonly a [[GRU cell]] or an [[LSTM cell]] (see the RNN Tutorial for an [[explanation]] of those). Encoder and decoder can share weights or, as is more common, use a different set of parameters. Multi-layer cells have been successfully used in [[sequence-to-sequence model]]s too, e.g. for translation [[Sutskever et al., 2014]] .


=== 2014a ===
=== 2014a ===
* ([[2014_SequencetoSequenceLearningwithN|Sutskever et al., 2014]]) ⇒ [[Ilya Sutskever]], [[Oriol Vinyals]], and [[Quoc V. Le]]. ([[2014]]). “[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Sequence to Sequence Learning with Neural Networks].” In: Advances in Neural Information Processing Systems.  
* ([[2014_SequencetoSequenceLearningwithN|Sutskever et al., 2014]]) ⇒ [[Ilya Sutskever]], [[Oriol Vinyals]], and [[Quoc V. Le]]. ([[2014]]). “[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Sequence to Sequence Learning with Neural Networks].” In: Advances in Neural Information Processing Systems.


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[[Category:Concept]]
[[Category:Concept]]

Latest revision as of 02:09, 5 August 2023

An LSTM-based Encoder-Decoder Network is an RNN/RNN-based encoder-decoder model composed of LSTM models (an LSTM encoder and an LSTM decoder).



References

2018

2017

2014a