Encoder-Decoder Seq2Seq (Sequence-to-Sequence) is a type of neural network architecture used for machine translation, speech recognition, and other natural language processing tasks. The ...

To make sequence-to-sequence predictions using a LSTM, we use an encoder-decoder architecture. The LSTM encoder-decoder consists of two LSTMs. The first LSTM, or the encoder, processes an input ...

Natural Language Processing has many interesting applications and Sequence to Sequence modelling is one of those interesting applications. It has major applications in question-answering systems and ...

While Seq2Seq and its variants achieve strong performance on various applications, a consistent interpretation of how the encoder-decoder structure is capable to embed the data for general time-series ...

To use this property, a dual attention-based encoder-decoder is developed in this article, which presents a customized sequence-to-sequence learning for soft sensor. We reveal that different quality ...

The ability of transformers to handle data sequences without the need for sequential processing makes them extremely effective for various NLP tasks, including translation, text summarization, and ...

To use this property, a dual attention-based encoder-decoder is developed in this article, which presents a customized sequence-to-sequence learning for soft sensor. We reveal that different quality ...