The attention-based encoder-decoder (AED) speech recognition model has been widely successful in recent years. However, the joint optimization of acoustic model and language model in end-to-end manner ...

I implement encoder-decoder based seq2seq models with attention. The encoder and the decoder are pre-attention and post-attention RNNs on both sides of the attention mechanism. Encoder:a RNN ...

The decoder takes the context vector produced by the encoder and generates an output sequence. Key points about the decoder include: Output Generation: It processes the context vector and generates an ...

Decoder-based LLMs can be broadly classified into three main types: encoder-decoder, causal decoder, and prefix decoder. Each architecture type exhibits distinct attention patterns. Encoder-Decoder ...

Large language models (LLMs) have changed the game for machine translation (MT). LLMs vary in architecture, ranging from decoder-only designs to encoder-decoder frameworks. Encoder-decoder models, ...

Quantile-Based Encoder-Decoder Deep Learning Models for Multi-Step Ahead Hydrological Forecasting [Conference presentation]. American Geophysical Union (AGU) Fall Meeting 2022, Online. Recent ...

Travel route recommendation is an important part of electronic tour guides and map applications. It aims to recommend a sequence of points of interest (POIs) to users based on their interests. The ...

In this article, we are going to see how we can remove noise from the image data using an encoder-decoder model. We will go through two approaches of denoising with encoder-decoder, one with dense ...