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

Neural network-assisted (NNA) Kalman filters provide an effective solution to addressing the filtering issues involving partially unknown system information by incorporating neural networks to compute ...

MicroCloud Hologram Inc. announces a noise-resistant Deep Quantum Neural Network architecture, advancing quantum computing and machine learning efficiency.

We have been stuck in using neural network models to achieve radar target recognition for so many years. I just want to be back to the 90s and 00s, when people could also achieve complex tasks with a ...

To this end, we propose a scheme that utilizes neural networks to extract effective information from long-time simulations of the non-Markovian stochastic Schrödinger equation, fine-tunes the networks ...

Hornik, K., Stinchcombe, M. and White, H. (1989) Multilayer Feedforward Networks Are Universal Approximators. Neural Networks, 2, 359-366.

ABSTRACT: We explore the performance of various artificial neural network architectures, including a multilayer perceptron (MLP), Kolmogorov-Arnold network (KAN), LSTM-GRU hybrid recursive neural ...

Create a fully connected feedforward neural network from the ground up with Python — unlock the power of deep learning!

By integrating memristors into a Hopfield neural network (HNN), a diverse range of dynamical behavior can be generated, which has significant implications for modeling and biomimetic applications of ...

Despite the widespread success of neural networks, their susceptibility to adversarial examples remains a significant challenge. Adversarial training (AT) has emerged as an effective approach to ...