Neural Sequence Model

Context:
- It can (typically) be implemented using Neural Sequence Architecture.
- It can include Encoder-Decoder Sequence Models.
- It can be trained using a Neural Sequence Model Training System (that may implement Backpropagation Through Time (BPTT)).
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Example(s):
- A seq2seq Model which utilizes an encoder to process the input sequence and a decoder to generate the output sequence.
- A Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) Model, or Gated Recurrent Unit (GRU) Model
- An Attention-based Neural Sequence Model, which employs an attention mechanism to weight the significance of different parts of the input sequence differently.
- A Selective State Space Model (SSM), which combines traditional state space modeling with neural network components.
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Counter-Example(s):
- A Feedforward Neural Network used for image classification, which does not model sequence data.
- A Convolutional Neural Network (CNN) used for processing static images.
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See: Sequence-to-Sequence Learning Task, Neural Language Model, Time Series Analysis, Deep Learning in Natural Language Processing.

References