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LiteLSTM Architecture Based on Weights Sharing for Recurrent Neural Networks
Nelly Elsayed, Zag ElSayed, Anthony S. Maida
Long short-term memory (LSTM) is one of the robust recurrent neural network architectures for learning sequential data. However, it requires considerable computational power to lea…
Deep Axial Hypercomplex Networks
Nazmul Shahadat, Anthony S. Maida
Over the past decade, deep hypercomplex-inspired networks have enhanced feature extraction for image classification by enabling weight sharing across input channels. Recent works m…
Enhancing ResNet Image Classification Performance by using Parameterized Hypercomplex Multiplication
Nazmul Shahadat, Anthony S. Maida
Recently, many deep networks have introduced hypercomplex and related calculations into their architectures. In regard to convolutional networks for classification, these enhanceme…
Deep Residual Axial Networks
Nazmul Shahadat, Anthony S. Maida
While convolutional neural networks (CNNs) demonstrate outstanding performance on computer vision tasks, their computational costs remain high. Several techniques are used to reduc…