Frontiers

Next-gen neuro-AI: Aligning foundation models and biologically plausible architectures with human cognition

Cognitive computational neuroscience has entered a transformative era. The rapid rise of large multimodal foundation models, state-space architectures, and ...

WiMi Explores the Application of Neural Networks in Parameter Optimization for Dual-Field Quantum Key Distribution

WiMi Hologram Cloud Inc. (NASDAQ: WiMi) (WiMi or the Company), a leading global Hologram Augmented Reality (AR) Technology ...

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 ...
IEEE

A Theoretical View of Linear Backpropagation and its Convergence

Abstract: Backpropagation (BP) is widely used for calculating gradients in deep neural networks (DNNs). Applied often along with stochastic gradient descent (SGD) or its variants, BP is considered as ...
Semiconductor Engineering

The Optical Implementation of Backpropagation (Oxford, Lumai)

A technical paper titled “Training neural networks with end-to-end optical backpropagation” was published by researchers at University of Oxford and Lumai Ltd. “Optics is an exciting route for the ...
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 ...
IEEE

ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines

Abstract: Deep Operator Networks (DeepONets) are among the most prominent frameworks for operator learning, grounded in the universal approximation theorem for operators. However, training DeepONets ...