6 citations · 14 across the 5 of their papers we have counts for
8 papers
Slim LSTM networks: LSTM_6 and LSTM_C6
Atra Akandeh, Fathi M. Salem
We have shown previously that our parameter-reduced variants of Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) are comparable in performance to the standard LSTM RNN…
Performance of Three Slim Variants of The Long Short-Term Memory (LSTM) Layer
Daniel Kent, Fathi M. Salem
The Long Short-Term Memory (LSTM) layer is an important advancement in the field of neural networks and machine learning, allowing for effective training and impressive inference p…
SLIM LSTMs
Fathi M. Salem
Long Short-Term Memory (LSTM) Recurrent Neural networks (RNNs) rely on gating signals, each driven by a function of a weighted sum of at least 3 components: (i) one of an adaptive…
Simplified Long Short-term Memory Recurrent Neural Networks: part III
Atra Akandeh, Fathi M. Salem
This is part III of three-part work. In parts I and II, we have presented eight variants for simplified Long Short Term Memory (LSTM) recurrent neural networks (RNNs). It is noted…
Simplified Long Short-term Memory Recurrent Neural Networks: part II
Atra Akandeh, Fathi M. Salem
This is part II of three-part work. Here, we present a second set of inter-related five variants of simplified Long Short-term Memory (LSTM) recurrent neural networks by further re…
Simplified Long Short-term Memory Recurrent Neural Networks: part I
Atra Akandeh, Fathi M. Salem
We present five variants of the standard Long Short-term Memory (LSTM) recurrent neural networks by uniformly reducing blocks of adaptive parameters in the gating mechanisms. For s…