Neural Machine Translation using LSTMs and Attention mechanism. Two approaches were implemented, models, one without out attention using repeat vector, and the other using encoder decoder architecture ...

Transformers have a versatile architecture that can be adapted beyond NLP. ... In tasks like translation, transformers manage context from past and future input using an encoder-decoder structure.

The Transformer architecture revolutionized NLP by replacing recurrent layers with attention mechanisms, enabling more efficient parallelization and better modeling of long-range dependencies. This ...

An encoder-decoder architecture is a powerful tool used in machine learning, specifically for tasks involving sequences like text or speech. It’s like a two-part machine that translates one form ...

Transformers combined with convolutional encoders have been recently used for hand gesture recognition (HGR) using micro-Doppler signatures. In this letter, we propose a vision-transformer-based ...

But not all transformer applications require both the encoder and decoder module. For example, the GPT family of large language models uses stacks of decoder modules to generate text.

Based on the vanilla Transformer model, the encoder-decoder architecture consists of two stacks: an encoder and a decoder. The encoder uses stacked multi-head self-attention layers to encode the input ...