An Encoder-decoder architecture in machine learning efficiently translates one sequence data form to another. ... especially for traditional recurrent neural network (RNN)-based encoders.

Zhu RC, Wang JF, Qiu TS, Yang DK, Feng B et al. Direct field-to-pattern monolithic design of holographic metasurface via residual encoder-decoder convolutional neural network. Opto-Electron Adv 6 ...

In brief, a transformer is a neural network architecture designed to model sequences of data, ... For both encoder and decoder architectures, the core component is the attention layer, ...

Specifically, we predict civilian unemployment using models based on four different neural network architectures. Each of these models outperforms benchmark models at short time horizons. One model, ...

Mu is built on a transformer-based encoder-decoder architecture featuring 330 million token parameters, making the SLM a good ...

One of the key conceptual differences between traditional coding and neural network coding is that the codec architecture allows much more freedom when using neural codecs. Since both encoder and ...