Encoder-decoder architecture

Q: How deeply does Optimization Algorithms: AI techniques for design, planning, and control problems cover encoder-decoder architecture?

Defines sequence-to-sequence modeling and pointer selection, but does not detail network internals.

As explained in Optimization Algorithms: AI techniques for design, planning, and control problems

A neural model that transforms an input sequence into an output sequence, typically allowing the two sequences to have different lengths.

A sequence-to-sequence model maps an ordered input sequence to an output sequence. In combinatorial problems such as sorting or the travelling salesperson problem, the output elements come from the input, so their possible categories depend on input length. section 11.3.3

Pointer networks address this challenge by using attention to select input items for the output. Unlike conventional attention, which helps generate an output element, this mechanism points to an input element. section 11.3.3

Selecting convex-hull points

Given points labeled [1, 2, 3, 4], a pointer network can estimate an output sequence such as [1, 4, 2]. The output can contain fewer items than the input. section 11.3.3

Table 11.2 Sequential ML (view table figure)

Task	Example
One-to-one	Image classification. We provide a single image as input, and the model outputs the classification or category, like “dog” or “cat,” as a single output.
One-to-many	Image captioning. We input a single image into the model, and it generates a sequence of words describing that image.
Many-to-one	Sentiment analysis. We input a sequence of words (like a sentence or a tweet), and the model outputs a single sentiment score (like “positive," “negative,” or “neutral”).
Many-to-many (type 1)	Sequence input and sequence output, like in the case of named entity recognition (NER). We input a sentence (a sequence of words), and the model outputs the recognized entity, such as a person, organization, location, etc.
Many-to-many (type 2), known as a synchronized sequence model	Synced sequence input and output. The model takes a sequence of inputs but doesn’t output anything until the entire sequence has been read. Then it outputs a sequence. An example of this is video classification, where the model takes a sequence of video frames as input and then outputs a sequence of labels for those frames.

Compare sequence tasks by whether they map one item or many to their outputs. section 11.3.3

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Also explained in

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Natural Language Processing in Action, Second EditionA neural architecture that encodes an input sequence into a representation and decodes that representation into an output sequence.
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Inside Deep Learning: Math, Algorithms, ModelsA neural sequence transduction model that maps an input sequence to an output sequence, potentially of different length, using an encoder and decoder.
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Deep Learning with Python, Third EditionA model that maps an input sequence to an output sequence, often used for translation or generation tasks.
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Knowledge Graphs and LLMs in ActionAn encoder-decoder architecture is a model design in which an encoder transforms input data into a learned representation and a decoder transforms that representation into predictions or reconstructed outputs.
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Deep Learning with R, Third EditionA sequence-to-sequence model predicts a target sequence from a separate source sequence, typically conditioning each target token on prior target tokens.
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Sutskever's ListA sequence-to-sequence model maps an input sequence to an output sequence using learned encoder and decoder components or an equivalent transformation.
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Causal AIA sequence-to-sequence model maps an input sequence to an output sequence, often for tasks such as translation, summarization, or conditional generation.
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Learn Generative AI with PyTorch
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Effective Conversational AIAn encoder-decoder architecture is a language-model architecture that encodes an input sequence and decodes a separate output sequence.

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Related concepts

BARTincludes · BART is a denoising sequence-to-sequence Transformer model with a bidirectional encoder and an autoregressive decoder.
decodehas part · Decoding is the process of transforming an encoded or internal representation into an interpretable or target-form output.
encoderhas part · An encoder is a component that transforms input data into an encoded representation for subsequent processing, reconstruction, comparison, or transmission.
attentionuses · Attention is a neural-network mechanism that computes input-dependent weights to combine information according to its relevance to a computation.
link predictionuses · Link prediction is a graph-learning task that estimates whether a connection exists or may occur between nodes, often by scoring candidate links.
retrieval-augmented generationused by · Retrieval-augmented generation is a technique that retrieves relevant information from external sources and supplies it as context to a generative model when producing an output.

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