In this paper, we first proposed a hybrid network expansion model to exploit the finegrained data parallelism. Based on the model, we implemented a ...

Abstract—With the rapidly increasing applications of deep learning, LSTM-RNNs are widely used. Meanwhile, the com- plex data dependence and intensive ...

In this paper, we first proposed a hybrid network expansion model to exploit the finegrained data parallelism. Based on the model, we implemented a ...

Jan 4, 2024 · A novel hybrid model was proposed, with Generalized Autoregressive Conditional Heteroscedasticity (GARCH) being replaced by Long Short-Term Memory (LSTM) ...

The application of long short-term memory (LSTM) operations enables neural networks to learn long-term dependencies between time series and sequence data, ...

In this hybrid architecture, recurrency aids the temporal memory of the inputs and output of the partial physics model, in a way that facilitates generalization.

Sep 5, 2023 · This research presents a hybrid model utilizing improved speech signals with dynamic feature breakdown using CNN and LSTM.

Missing: expansion | Show results with:expansion

Jun 13, 2023 · LSTM is a type of neural network that is commonly used for sequential data such as time series, speech recognition, and natural language processing.

This study proposes innovative hybrid models that integrate a convolutional neural network (CNN) with a long short-term memory (LSTM) neural network and a gated ...