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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…
Simplified Minimal Gated Unit Variations for Recurrent Neural Networks
Joel Heck, Fathi M. Salem
Recurrent neural networks with various types of hidden units have been used to solve a diverse range of problems involving sequence data. Two of the most recent proposals, gated re…
Simplified Gating in Long Short-term Memory (LSTM) Recurrent Neural Networks
Yuzhen Lu, Fathi M. Salem
The standard LSTM recurrent neural networks while very powerful in long-range dependency sequence applications have highly complex structure and relatively large (adaptive) paramet…