communication overhead is always a major challenge in distributed deep learning. To cope with this challenge, gradient compression has been used to reduce the amount of data to be exchanged. However, ...

Gradient Descent is a widely used optimization algorithm in machine learning and deep learning to minimize the error (or loss) of a model. In simpler terms, it helps ...

model to describe the distributed synchronous stochastic gradient descent (S-SG D) algorithm, which has been widely used in distributed deep learning frameworks. To understand the practical impact of ...

This study aims to enhance the spatial resolution and accuracy of bathymetric prediction by integrating Gravity Anomaly (GA) and Vertical Gravity Gradient Anomaly (VGG) data with a dual ... In future ...

Engineers use 'deep learning' techniques to speed ... and multilayer perceptrons that take minimal data from the simulated structures of 2D materials and make "reasonably accurate" predictions ...

The findings are published in Applied Materials Today ("Deep ... of 2D materials classification. (Image: Yaping Qi et al.) The spectral data from seven different 2D materials and three distinct ...