Seq2Seq is essentially an abstract deion of a class of problems, rather than a specific model architecture, just as the ...

In recent years, with the rapid development of large model technology, the Transformer architecture has gained widespread attention as its core cornerstone. This article will delve into the principles ...

An Encoder-decoder architecture in machine learning efficiently translates one sequence data form to another.

EnCodec is a streaming, convolutional-based encoder-decoder architecture with three principal components: 1) an encoder network that inputs an audio extract and outputs a latent representation; 2) a ...

We propose a single-document abstractive summarization system that integrates token relation into a traditional RNN-based encoder-decoder architecture. We employ pointer-wise mutual information to ...

Encoder-decoder recurrent neural network models (RNN Seq2Seq) have achieved success in ubiquitous areas of computation and applications. They were shown to be effective in modeling data with both ...

This project implements an Automatic Image Captioning model based on encoder-decoder CNN-RNN architecture trained on COCO dataset. Model is able to generate statements about input image.

PyTorch implementation of recurrent neural network encoder-decoder architecture model for statistical machine translation, as detailed in this paper: Learning Phrase Representations using RNN ...