In the world of particle physics, where scientists unravel the mysteries of the universe, artificial intelligence (AI) and ...

However, RNNs suffer from fading and exploding gradient problems ... common in both RNN-based and transformer-based models. Attention mechanisms, especially in transformer models, have significantly ...

"Recently, there has been a lot of research on different *pre-training* objectives for transformer-based encoder-decoder models, *e.g.* T5, Bart, Pegasus, ProphetNet, Marge, *etc*..., but the model ...

To reduce complexity, we apply an encoder-decoder recurrent neural network (ED-RNN) as a machine learning model to the function migration scheduling problem. Performance evaluations show that the ...

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Before transformers, recurrent neural networks (RNN) were the go-to solution ... The task of the decoder module is to translate the encoder’s attention vector into the output data (e.g., the ...