Recurrent neural networks, or RNNs, ... They use a dense feedforward network as a sub-neural net inside the encoder and decoder components. They also demand considerable computing power.

There are four very successful and widely adopted deep learning paradigms: LSTM units in recurrent neural networks, convolutional layers in convolutional neural networks (CNNs), encoder-decoder ...

Related Stories. AlphaQubit, a recurrent-transformer-based neural network, significantly outperformed previous decoders, including ML-based ones, in decoding Sycamore surface code experiments.

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 ...

Owing to the immense popularity of ray-tracing and path tracing rendering algorithms for visual effects, there has been a surge of interest in developing filtering and reconstruction methods to deal ...

Their hypothesis was tested using recurrent neural networks trained on a decision-making task. Participants—humans, monkeys, or computers—viewed a shape (square or triangle) followed by a ...