Sequence-to-Sequence Network
A Sequence-to-Sequence Network is an artificial neural network/encoder-decoder neural network composed of a decoder RNN and a decoder RNN.
- Context:
- It can be an input to a seq2seq Modeling System (that implements a seq2seq algorithm).
- It can range from being a Single-Layer Cell Sequence-to-Sequence Network to being a Multi-Layer Cells Sequence-to-Sequence Network.
- Example(s):
- See: Neural seq2seq Algorithm, LSTM Network.
References
2017
- Sean Robertson. (2017). “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.
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 models 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.