Encoder - Decoder model is a Machine Learning model comprising of two learning components (two neural networks in this context) called Encoder and Decoder. The first network works normally, and the second network works in reverse manner

The decoder, just like the encoder-decoder RNN, is essentially a conditional language model that attends to the encoder representation and generates the target words one by one, at each timestep conditioning on the source sentence and the …

By introducing Ws to the score, we are giving the network the ability to learn which aspects of similarity between the decoder and encoder states are important to the current application.

Sequence-to-Sequence Model • The interface between encoder and decoder is a single vector regardless the sentence length. Ilya Sutskever, Oriol Vinyals, and Quoc V Le. Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems 27, pages 3104–3112, Montreal, Canada, December 2014

In this work, we investigate how encoder-decoder networks solve different sequence-to-sequence tasks. We introduce a way of decomposing hidden states over a sequence into temporal (independent of input) and input-driven (independent of sequence position) components.

Basic encoder-decoder architecture Two separate RNN models Encoder: maps from an input sequence x to an intermediate representation, the context Decoder: maps from the context sequence y. Commonly known as sequence-to-sequence (seq2seq) models

The most recent paradigm of MT models, starting in around 2014, is referred to as neural machine translation (NMT). In this note, we will use the running example of NMT as a way to look at encoder-decoder models (also called sequence-to-sequence models) and attention.

Here, we create an encoder model which returns its internal states. The decoder model will then use the decoder's internal states from the previous timestep and the decoder's output from the previous timestep to compute the output and hidden states of the current timestep.

In this work we study the baseline encoder-decoder framework in machine translation and take a brief look at the encoder structures proposed to cope with the difficulties of fea-ture extraction. Furthermore, an empirical study of solutions to enable decoders to generate richer fine-grained output sentences is provided.

ViT model attains state-of-the-art performance on multiple popular benchmarks, including 88.55% top-1 accuracy on ImageNet and 99.50% on CIFAR-10 Transformers for Image Recognition at Scale