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Found 7 papers, 2 papers with code
Stein Variational Gradient Descent-based Detection For Random Access With Preambles In MTC
Traditional preamble detection algorithms have low accuracy in the grant-based random access scheme in massive machine-type communication (mMTC).
A Hybrid Quantum-Classical Approach based on the Hadamard Transform for the Convolutional Layer
In this paper, we propose a novel Hadamard Transform (HT)-based neural network layer for hybrid quantum-classical computing.
Multichannel Orthogonal Transform-Based Perceptron Layers for Efficient ResNets
Trainable soft-thresholding layers, that remove noise in the transform domain, bring nonlinearity to the transform domain layers.
Input Normalized Stochastic Gradient Descent Training of Deep Neural Networks
We evaluate the efficiency of our training algorithm on benchmark datasets using ResNet-18, WResNet-20, ResNet-50, and a toy neural network.
DCT Perceptron Layer: A Transform Domain Approach for Convolution Layer
In this paper, we propose a novel Discrete Cosine Transform (DCT)-based neural network layer which we call DCT-perceptron to replace the $3\times3$ Conv2D layers in the Residual neural Network (ResNet).
Classification of the Cervical Vertebrae Maturation (CVM) stages Using the Tripod Network
They also have a built-in set of novel directional filters that highlight the Cervical Verte edges in X-ray images.
Multipod Convolutional Network
In this paper, we introduce a convolutional network which we call MultiPodNet consisting of a combination of two or more convolutional networks which process the input image in parallel to achieve the same goal.
