Feb 20, 2017 · In this paper, we propose a novel neural network structure, namely feedforward sequential memory networks (FSMN), to model long-term ...
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In this paper, we propose to use a more general type of a-priori knowledge, namely that the temporal dependencIes are structured hierarchically. This implies ...
Recurrent neural networks (RNNs) are a class of artificial neural networks for sequential data processing. Unlike feedforward neural networks, which process ...
Nov 27, 2021 · ... Neural Networks (DuRNN). The DuRNN consists of two parts to learn the short-term dependence and progressively learn the long-term dependence.
A recurrent neural network (RNN) is a deep learning model that is trained to process and convert a sequential data input into a specific sequential data ...
May 28, 2019 · Title:Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics.
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Mar 16, 2023 · Here, we showed how a recurrent network with local learning rules can implement the successor representation, a predictive algorithm that ...
Recurrent neural networks (RNNs) use sequential data to solve common temporal problems seen in language translation and speech recognition.
Experimental results on acoustic modeling and language modeling tasks have shown that FSMN can significantly outperform the recurrent neural networks and ...
Dynamic systems usually have long-term dependency. Recent work, however, shows that the long-term memory models like recurrent neural networks and ...