In 1986, three scientists improved backpropagation, making neural networks learn effectively from data

A groundbreaking 1986 technique called backpropagation revolutionized artificial intelligence, enabling computers to learn ...
Tech Xplore

Brain-inspired AI architecture could compute faster while using far less power

Spiking neural networks (SNNs) are artificial intelligence (AI) models inspired by how biological neurons communicate with ...
Nature

Backpropagation-free spiking neural networks with the forward–forward algorithm

Spiking Neural Networks (SNNs) offer a biologically inspired computational paradigm that emulates neuronal activity through discrete spike-based processing. Despite their advantages, training SNNs ...
eLife

Adjoint propagation of error signal through modular recurrent neural networks for biologically plausible learning

Biologically plausible learning mechanisms have implications for understanding brain functions and engineering intelligent systems. Inspired by the multi-scale recurrent connectivity in the brain, we ...
Nature

Enhanced cricket match prediction using kernel methods for feature extraction and back-propagation neural networks

This study presents a dynamic machine learning framework for predicting the outcome of One Day International (ODI) cricket matches by analysing match progression at multiple game states. Each over is ...
New Scientist

Quantum neural network may be able to cheat the uncertainty principle

The Heisenberg uncertainty principle puts a limit on how precisely we can measure certain properties of quantum objects. But researchers may have found a way to bypass this limitation using a quantum ...
EurekAlert!

Deep neural networks enable accurate pricing of American options under stochastic volatility

It shows the schematic of the physics-informed neural network algorithm for pricing European options under the Heston model. The market price of risk is taken to be λ=0. Automatic differentiation is ...
IEEE

Adaptive Totem Pole PFC Control Based on Backpropagation Neural Network With Wide Voltage Range and Low Current THD

Abstract: The totem pole power factor correction (PFC) circuit is a prevalent front-end topology employed in on-board chargers owing to its high efficiency and uncomplicated structure. Nevertheless, ...
Scientific Research Publishing

Asaad, R.R. and Ali, R.I. (2019) Back Propagation Neural Network (BPNN) and Sigmoid Activation Function in Multi-Layer Networks. Academic Journal of Nawroz University, 8, 216-221.

ABSTRACT: The accurate prediction of backbreak, a crucial parameter in mining operations, has a significant influence on safety and operational efficiency. The occurrence of this phenomenon is ...
Scientific Research Publishing

Suliman, A. and Zhang, Y. (2015) A Review on Back-Propagation Neural Networks in the Application of Remote Sensing Image Classification. Journal of Earth Science and ...

ABSTRACT: The accurate prediction of backbreak, a crucial parameter in mining operations, has a significant influence on safety and operational efficiency. The occurrence of this phenomenon is ...
Phys.org

Algorithm precisely quantifies flow of information in complex networks

Networks are systems comprised of two or more connected devices, biological organisms or other components, which typically share information with each other. Understanding how information moves ...
techxplore

VFF-Net algorithm provides promising alternative to backpropagation for AI training

Deep neural networks (DNNs), which power modern artificial intelligence (AI) models, are machine learning systems that learn hidden patterns from various types of data, be it images, audio or text, to ...