New Scientist

Quantum neural network may be able to cheat the uncertainty principle

Calculations show that injecting randomness into a quantum neural network could help it determine properties of quantum ...

WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning

WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning BEIJING, Jan. 05, 2026––WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the ...
Learn With Jay on MSN

Neural networks explained: Forward and backward propagation simplified

In this video, we will understand forward propagation and backward propagation. Forward propagation and backward propagation ...
eLife

A differentiable model for optimizing the genetic drivers of synaptogenesis

This study presents SynaptoGen, a differentiable extension of connectome models that links gene expression, protein-protein interaction probabilities, synaptic multiplicity, and synaptic weights, and ...
Learn With Jay on MSN

Backpropagation through time explained for RNNs

In this video, we will understand Backpropagation in RNN. It is also called Backpropagation through time, as here we are ...
IEEE

Bi-PIL: Bidirectional Gradient-Free Learning Scheme for Multilayer Neural Networks

Abstract: Training deep neural networks typically relies on gradient descent learning schemes, which is usually time-consuming, and the design of complex network architectures is often intractable. In ...
IEEE

Regularized over-parametrized neural networks learned by gradient descent can generalize well

Abstract: Estimation of univariate regression function by a neural network with one hidden layer is considered, where the weight vector is determined by applying gradient descent to a regularized ...
marktechpost

Hypernetwork Fields: Efficient Gradient-Driven Training for Scalable Neural Network Optimization

Hypernetworks have gained attention for their ability to efficiently adapt large models or train generative models of neural representations. Despite their effectiveness, training hyper networks are ...