资讯

Semiconductor Engineering16 小时

How Neural Super Sampling Works: Architecture, Training, And Inference

In 540p-to-1080p comparisons, NSS improves stability and detail retention. It performs well in scenes with fast motion, ...

DNA-based neural network learns from examples to solve problems

Neural networks are computing systems designed to mimic both the structure and function of the human brain. Caltech ...
IEEE5月

Adaptive Neural Network Algorithm with Reinforcement Learning

Abstract: Neural network algorithm (NNA) is a new type of population-based meta-heuristic algorithm, which is inspired by artificial neural networks. NNA is a result of the deep integration of ...
Frontiers1 年

Stable tensor neural networks for efficient deep learning

Learning from complex, multidimensional data has become central to computational mathematics, and among the most successful high-dimensional function approximators are deep neural networks (DNNs).
Frontiers6 年

BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python

Biologically Inspired Neural and Dynamical Systems Laboratory, College of Computer and Information Sciences, University of Massachusetts Amherst, Amherst, MA, United States Several software packages ...
PNAS11 年

A self-learning magnetic Hopfield neural network with intrinsic gradient descent adaption

Natural physical systems evolve with certain global quantities being minimized or maximized due to physical laws. For example, charges in conductors redistribute to reach electrostatic equilibrium, ...