IEEE

Fast-Converging Volterra Equalizer Using Mini-Batch Gradient Descent-based Adam Optimization

Abstract: We propose an adaptive moment estimation (Adam)-based 2 nd-order Volterra nonlinear equalizer (VNLE) employing a mini-batch gradient descent (MGD) algorithm for intensity ...
IEEE

Low-Complexity Inverse Matrix Mini-Batch Gradient Descent for MIMO Equalization and Precoding

Abstract: As the size of base station antenna arrays continues to grow, even with linear processing algorithms, the computational complexity and power consumption required for massive MIMO ...
GitHub

mini-batch-gradient-descent

Performing gradient descent for calculating slope and intercept of linear regression using sum square residual or mean square error loss function. A "from-scratch" 2 ...
marktechpost

Privacy Implications and Comparisons of Batch Sampling Methods in Differentially Private Stochastic Gradient Descent (DP-SGD)

Differentially Private Stochastic Gradient Descent (DP-SGD) is a key method for training machine learning models like neural networks while ensuring privacy. It modifies the standard gradient descent ...
Scientific Research Publishing

Hinton, G., Srivastava, N. and Swersky, K. (2012) Neural Networks for Machine Learning Lecture 6a Overview of Mini-Batch Gradient Descent.

ABSTRACT: The development of artificial intelligence (AI), particularly deep learning, has made it possible to accelerate and improve the processing of data collected in different fields (commerce, ...
GitHub

mini-batch

Tensorflow implementation of asyncronous 1-step Q learning in "Asynchronous Methods for Deep Reinforcement Learning" with improvement on weight update process (use minibatch) to speed up training.