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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 ...
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.
It uses an encoder-decoder to accomplish few-shot learning. The encoder outputs a representation of the input that the decoder uses as input to perform a specific task, such as translating the input into another language. The model outperforms the much larger GPT-3 in language translation and summarization.
The iterative decoding algorithm works best when there are not short cycles in the factor graph that represents the decoder; the interleaver is chosen to avoid short cycles. Interleaver designs include: rectangular (or uniform) interleavers (similar to the method using skip factors described above) convolutional interleavers
t. e. An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data ( unsupervised learning ). [1] [2] An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.
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.
The decoder must have the same model as the encoder. Encoding and decoding: overview. In general, each step of the encoding process, except for the last, is the same; the encoder has basically just three pieces of data to consider: The next symbol that needs to be encoded
Entropy coding. In information theory, an entropy coding (or entropy encoding) is any lossless data compression method that attempts to approach the lower bound declared by Shannon's source coding theorem, which states that any lossless data compression method must have an expected code length greater than or equal to the entropy of the source.