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 ...

Researchers have developed an algorithm to train an analog neural network just as accurately as a digital one, enabling the development of more efficient alternatives to power-hungry deep learning ...

Recently, a research team led by Prof. GAO Xiaoming from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences, developed an intelligent neural network algorithm that effectively ...

Engineers have uncovered an unexpected pattern in how neural networks -- the systems leading today's AI revolution -- learn, suggesting an answer to one of the most important unanswered questions in ...

Modern neural networks, with billions of parameters, are so overparameterized that they can "overfit" even random, ...

Obtaining the gradient of what's known as the loss function is an essential step to establish the backpropagation algorithm developed by University of Michigan researchers to train a material. The ...

We have explained the difference between Deep Learning and Machine Learning in simple language with practical use cases.

The rise of artificial intelligence (AI) deep learning algorithms is helping to accelerate brain-computer interfaces (BCIs). Published in this month’s Nature Neuroscience is new research that shows ...