This deep dive covers the full mathematical derivation of softmax gradients for multi-class classification. #Backpropagation ...

Matthew Leming, Ph.D., and Hyungsoon Im, Ph.D. of the Center for Systems Biology at Massachusetts General Hospital, are the ...

This paper presents a novel adaptive learning-rate backpropagation neural network (ALR-BPNN) algorithm based on the minimization of mean-square deviation (MSD) to implement a fast convergence rate and ...

Keywords: spiking neural network, convolutional neural network, spike-based learning rule, gradient descent backpropagation, leaky integrate and fire neuron Citation: Lee C, Sarwar SS, Panda P, ...

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

A new technical paper titled “Hardware implementation of backpropagation using progressive gradient descent for in situ training of multilayer neural networks” was published by researchers at ...

Additionally, the influence of coupling interactions complicates the isolation of local node dynamics. Given the architectural similarities between dynamical networks and recurrent neural networks ...

In this regard, Hinton proposes the FF algorithm as an alternative to backpropagation for neural network learning. The FF algorithm is inspired by Boltzmann machines (Hinton and Sejnowski, 1986) and ...

The spiking neural network (SNN) is a possible pathway for low-power and energy-efficient processing and computing exploiting spiking-driven and sparsity features of biological systems. This article ...