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They can translate sequences from one domain to another, such as text to speech.
They only work with fixed-length input sequences and cannot handle variable lengths.
They utilize attention mechanisms to improve context understanding in sequences.
They are limited to simple linear transformations of input data.
They can be used for tasks like machine translation, text summarization, and image captioning.
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Sequence transduction models convert a whole input sequence into another sequence, such as translating words or turning text into speech. Other options are incorrect because The belief that the models can only read fixed‑length sentences is wrong; Some think these models only do straight‑line math.
Key Concepts
Sequence Transduction Models
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Definition
Sequence transduction models are based on complex neural networks that encode and decode sequences. These models aim to translate input sequences into output sequences and have seen advancements in performance and efficiency.
Topic Definition
Sequence transduction models are based on complex neural networks that encode and decode sequences. These models aim to translate input sequences into output sequences and have seen advancements in performance and efficiency.
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