GitHub

A novel backpropagation algorithm based on negated kurtosis loss for training shallow, convolutional, and deep neural networks

The conventional backpropagation (BP) algorithm remains the most widely used approach for training neural networks (NNs), including shallow NN (SNN), convolutional NN (CNN), deep NN (DNN), and deep ...
GitHub

MATLAB+Simulink example implementing the backpropagation algorithm to reconstruct the unknown input of a system with distributed parameters, based on a known model and output data

Abstract: The task involves reconstructing an unknown signal applied to the input of a dynamic system with distributed parameters. A feedforward neural network is used for signal reconstruction. The ...
marktechpost

On-Chip Implementation of Backpropagation for Spiking Neural Networks on Neuromorphic Hardware

Natural neural systems have inspired innovations in machine learning and neuromorphic circuits designed for energy-efficient data processing. However, implementing the backpropagation algorithm, a ...
Quanta Magazine

Neuron Bursts Can Mimic Famous AI Learning Strategy

A new model of learning centers on bursts of neural activity that act as teaching signals — approximating backpropagation, the algorithm behind learning in AI. Every time a human or machine learns how ...
Semiconductor Engineering

Backpropagation Algorithm On Neuromorphic Spiking HW (U. Of Zurich, ETH Zurich, LANL)

A new technical paper titled “The backpropagation algorithm implemented on spiking neuromorphic hardware” was published by University of Zurich, ETH Zurich, Los Alamos National Laboratory, Royal ...