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 ...

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 ...

Abstract: Low-dose computed tomography (LDCT) images frequently suffer from noise and artifacts due to diminished radiation doses, challenging the diagnostic integrity of the images. We introduce an ...