The Encoder-Decoder RNN is a powerful architecture for problems where both the input and the output are sequences, such as machine translation, text summarization, and question answering. The notebook ...

Encoder-Decoder LSTM Machine Translation Model Overview This repository contains the implementation of a machine translation model using Encoder-Decoder architecture with Long Short-Term Memory (LSTM) ...

In this article, we are going to see how we can remove noise from the image data using an encoder-decoder model.

Text generation is crucial for many applications in natural language processing. With the prevalence of deep learning, the encoder-decoder architecture is dominantly adopted for this task. Accurately ...

Diffusion models, integral in text-to-video and reference-guided image generation, leverage the UNet architecture, comprising an encoder, bottleneck, and decoder. While past research focused on the ...

This is followed by an introduction of our encoder model GI-KBGAT, an improved Graph Attention Network for KG, which considers gate mechanism on multi-head attention and interaction between entities ...

Call it the return of Clippy — this time with AI. Microsoft’s new small language model shows us the future of interfaces.