Abstract: The gradient descent bit-flipping with momentum (GDBF-w/M) and probabilistic GDBF-w/M (PGDBF-w/M) algorithms significantly improve the decoding performance of the bit-flipping (BF) algorithm ...

ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...

Abstract: Distributed gradient descent algorithms have come to the fore in modern machine learning, especially in parallelizing the handling of large datasets that are distributed across several ...

Learn how gradient descent really works by building it step by step in Python. No libraries, no shortcuts—just pure math and code made simple. It Sits on a Vast Haul of Mineral Wealth. Now This Arctic ...

Stochastic gradient descent (SGD) provides a scalable way to compute parameter estimates in applications involving large-scale data or streaming data. As an alternative version, averaged implicit SGD ...

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, ...

a) Conceptual diagram of the on-chip optical processor used for optical switching and channel decoder in an MDM optical communications system. (b) Integrated reconfigurable optical processor schematic ...

Stochastic gradient descent (SGD), an important optimization method in machine learning, is widely used for parameter estimation especially in online setting where data comes in stream. While this ...