Jan 2, 2021 · The Encoder-Decoder attention layer works like Self-attention, except that it combines two sources of inputs – the Self-attention layer below it as well as the output of the Encoder stack. The Self-attention output is passed into a Feed-forward layer, which then sends its output upwards to the next Decoder.

Encoder-decoder architectures can handle inputs and outputs that both consist of variable-length sequences and thus are suitable for sequence-to-sequence problems such as machine translation. The encoder takes a variable-length sequence as input and transforms it into a state with a fixed shape.

The encoder is an LSTM that takes in a sequence of tokens and turns it into a vector. The decoder is another LSTM that converts the vector into a sequence of tokens. Similarly, another 130M-parameter model used gated recurrent units (GRU) instead of LSTM. [22] .

Feb 27, 2025 · The transformer model is built on an encoder-decoder architecture where both the encoder and decoder are composed of a series of layers that utilize self-attention mechanisms and feed-forward neural networks.

Jun 17, 2023 · Fundamentally, both encoder- and decoder-style architectures use the same self-attention layers to encode word tokens. However, the main difference is that encoders are designed to learn embeddings that can be used for various predictive modeling tasks …

Oct 1, 2024 · Each encoder consists of two layers: the self-attention layer (or self-attention mechanism) and the feed-forward neural network. The first layer guides the encoder in surveying and focusing on other related words in a given input as it encodes one specific word therein.

Feb 1, 2021 · The encoder-decoder model is a way of organizing recurrent neural networks for sequence-to-sequence prediction problems or challenging sequence-based inputs like texts [ sequence of words ...

Mar 10, 2021 · There are three main blocks in the encoder-decoder model, The Encoder will convert the input sequence into a single-dimensional vector (hidden vector). The decoder will convert the hidden...

In 2014, Cho et al. and Sutskever et al. proposed to use an encoder-decoder model purely based on recurrent neural networks (RNNs) for sequence-to-sequence tasks. In contrast to DNNS, RNNs are...

The Decoder Layer: How It Differs. On the flip side lies the decoder section, which serves a distinct purpose in the Transformer architecture. While it also comprises self-attention and feedforward components, the decoder integrates an additional layer—the encoder-decoder attention mechanism.