The hyperbolic tangent function returns values between -1 and +1, so arithmetic overflow is not a problem. Here the input value is checked merely to improve performance. The static utility methods in class Helpers are just coding conveniences. The MakeMatrix method used to allocate matrices in t...
Deep High Order Tensor Convolutional Sparse Coding for Hyperspectral Image Classification. IEEE Trans. Geosci. Remote Sens. 2022, 60, 1–11. [Google Scholar] [CrossRef] Cheng, C.; Li, H.; Peng, J.; Cui, W.; Zhang, L. Hyperspectral Image Classification Via Spectral-Spatial Random Patches...
It begins with the offline generation of a random bit sequence using MATLAB, which is subsequently mapped to PAM symbols through gray coding. These PAM symbols undergo upsampling and processing via a non-return-to-zero (NRZ) rectangular pulse-shaping filter. The resulting signal is then loaded ...
The critical feature of the function is that regardless of the input, the output will fall between 0 and 1. This feature is very handy in coding a neural network because the output of a neuron can always be expressed in a range between full on and full off. The activation function is s...
As the network configuration in a practical setup may change dynamically within milliseconds, the proposed NRX architecture is adaptive and capable of supporting different modulation and coding schemes (MCS) without the need for any re-training and without introducing any additional inference complexity....
Despite the great potential of deep neural networks (DNNs), they require massive weights and huge computational resources, creating a vast gap when deploying artificial intelligence at low-cost edge devices. Current lightweight DNNs, achieved by high-dim
The static utility methods in class Helpers are just coding conveniences. The MakeMatrix method used to allocate matrices in the NeuralNetwork constructor allocates each row of a matrix implemented as an array of arrays: XMLCopy public static double[][] MakeMatrix(int rows, int cols) ...
Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv 2015, arXiv:1510.00149. [Google Scholar] Molchanov, P.; Tyree, S.; Karras, T.; Aila, T.; Kautz, J. Pruning convolutional neural networks for resource efficient inference. arXiv 2016...
Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv 2015, arXiv:1510.00149. [Google Scholar] Molchanov, P.; Tyree, S.; Karras, T.; Aila, T.; Kautz, J. Pruning convolutional neural networks for resource efficient inference. arXiv 2016...
Nevertheless, spike sparsity and a single spike encoding strategies, such as inter-spike-interval coding [179], time-to-first-spike [180], or time-delay coding [181], hold great potential in reducing the communication and the energy of neural-inspired information processing systems. Today, the...