Wolfram-like attention framing meets spiking networks: event-triggered, energy-thrifty AI that “wakes” to stimuli.

Biology-inspired, silicon-based computing may boost AI efficiency; AMP2 instead uses AI to accelerate anaerobic biology.

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are the ...

Researchers from Zhejiang Lab have proposed a novel spatiotemporal mode multiplexing technology, coupling pulsed orbital angular momentum (OAM) beams with diffractive deep neural networks (D2NN) and ...

AI has successfully been applied in many areas of science, advancing technologies like weather prediction and protein folding ...

“Neural networks are currently the most powerful tools in artificial intelligence,” said Sebastian Wetzel, a researcher at the Perimeter Institute for Theoretical Physics. “When we scale them up to ...

An MIT spinoff co-founded by robotics luminary Daniela Rus aims to build general-purpose AI systems powered by a relatively new type of AI model called a liquid neural network. The spinoff, aptly ...

Machine learning techniques that make use of tensor networks could manipulate data more efficiently and help open the black ...

郭义销，明平兵，于灏高维薛定谔特征值问题在诸多科学和工程领域中起着至关重要的作用。然而，由于维数灾难和奇异势函数等困难，精确求解这一问题面临巨大挑战。因此，为该问题设计高精度的高效计算方法具有重要意义。针对高维区域上薛定谔算子的Dirichlet特征值问题，我们提出了一种求解任意阶本征值和本征态的机器学习方 ...