The data needs some cleaning before being used to train our neural translation model. I implement encoder-decoder based seq2seq models with attention. The encoder and the decoder are pre-attention and ...

An Encoder-Decoder model is a fundamental architecture in the field of deep ... and transforms it into a continuous representation. Recurrent or Transformer-Based: Encoders can be based on recurrent ...

encoder-decoder, causal decoder, and prefix decoder. Each architecture type exhibits distinct attention patterns. Based on the vanilla Transformer model, the encoder-decoder architecture consists of ...

For example, you might use a multi-task learning approach, where you train your encoder-decoder model on multiple related tasks simultaneously, and share some parameters across them. This can ...

as encoder-decoder models do, they are highly capable of generating fluent text. This makes them particularly good at text generation tasks — like completing a sentence or generating a story based on ...

This work proposes an SNN-based encoder-decoder model to improve the recognition performance of AER objects. An STDP-based locally connected spiking neural network (LC-SNN) is proposed as an encoder ...

Jahangir, M.S. , You, J. , & Quilty, J.. (2022, December 12-16). Quantile-Based Encoder-Decoder Deep Learning Models for Multi-Step Ahead Hydrological Forecasting [Conference presentation]. American ...

In this article, we propose an encoder-decoder-based travel route recommendation framework ... Multiple explicit requirements can be supported in our model, including unavailable POIs, mandatory POIs, ...