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Encoder-decoder architecture. Like earlier seq2seq models, the original transformer model used an encoder-decoder architecture. The encoder consists of encoding layers that process the input tokens iteratively one layer after another, while the decoder consists of decoding layers that iteratively process the encoder's output as well as the ...
In the process of encoding, the sender (i.e. encoder) uses verbal (e.g. words, signs, images, video) and non-verbal (e.g. body language, hand gestures, face expressions) symbols for which he or she believes the receiver (that is, the decoder) will understand. The symbols can be words and numbers, images, face expressions, signals and/or actions.
The encoder-decoder architecture, often used in natural language processing and neural networks, can be scientifically applied in the field of SEO (Search Engine Optimization) in various ways: Text Processing: By using an autoencoder, it's possible to compress the text of web pages into a more compact vector representation. This can help reduce ...
In this regard, Berlo speaks of the source-encoder and the decoder-receiver. Treating the additional components separately is especially relevant for technical forms of communication. For example, in the case of a telephone conversation, the message is transmitted as an electrical signal and the telephone devices act as encoder and decoder.
BERT is an "encoder-only" transformer architecture. At a high level, BERT consists of three modules: Embedding: This module converts an array of one-hot encoded tokens into an array of real-valued vectors representing the tokens. It represents the conversion of discrete token types into a lower-dimensional Euclidean space.
The attention mechanism is an enhancement introduced by Bahdanau et al. in 2014 to address limitations in the basic Seq2Seq architecture where longer input sequence results in the hidden state output of the encoder become irrelevant for the decoder. It enables the model to selectively focus on different parts of the input sequence during the ...
Download as PDF; Printable version; Appearance. ... , but due to the computational effort in implementing encoder and decoder and the introduction of Reed–Solomon ...
Convolutional codes are often characterized by the base code rate and the depth (or memory) of the encoder . The base code rate is typically given as , where n is the raw input data rate and k is the data rate of output channel encoded stream. n is less than k because channel coding inserts redundancy in the input bits.