9 citations · 35 across the 10 of their papers we have counts for
12 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…
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…
A Basic Recurrent Neural Network Model
Fathi M. Salem
We present a model of a basic recurrent neural network (or bRNN) that includes a separate linear term with a slightly "stable" fixed matrix to guarantee bounded solutions and fast…
Two Pairwise Iterative Schemes For High Dimensional Blind Source Separation
Zaid Albataineh, Fathi M. Salem
This paper addresses the high dimensionality problem in blind source separation (BSS), where the number of sources is greater than two. Two pairwise iterative schemes are proposed…
A Convex Cauchy-Schwarz DivergenceMeasure for Blind Source Separation
Zaid Albataineh, Fathi M. Salem
We propose a new class of divergence measures for Independent Component Analysis (ICA) for the demixing of multiple source mixtures. We call it the Convex Cauchy-Schwarz Divergence…
A Blind Adaptive CDMA Receiver Based on State Space Structures
Zaid Albataineh, Fathi M. Salem
Code Division Multiple Access (CDMA) is a channel access method, based on spread-spectrum technology, used by various radio technologies world-wide. In general, CDMA is used as an…