For the past few years, the recipe for building smarter artificial intelligence has been simple: make it bigger. Add more ...

我尝试着用让你爷爷奶奶都能理解的方式来描述mHC架构。 如果想理解mHC，先要知道HC（Hyper-Connections)；如果想理解HC，先要知道Residuals（残差）；要理解Residuals，先要知道深度神经元网络（Deep Neural Network)。 所以，有这样的一个认知递进： Deep Neural Network -> ...

Beijing, Jan. 05, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Next-Generation Quantum Convolutional Neural Network Technology for Multi-Channel Supervised Learning ...

During my first semester as a computer science graduate student at Princeton, I took COS 402: Artificial Intelligence. Toward the end of the semester, there was a lecture about neural networks. This ...

A new technical paper titled “Solving sparse finite element problems on neuromorphic hardware” was published by researchers ...

The simplified approach makes it easier to see how neural networks produce the outputs they do. A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher.

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write ...

“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 ...

The initial research papers date back to 2018, but for most, the notion of liquid networks (or liquid neural networks) is a new one. It was “Liquid Time-constant Networks,” published at the tail end ...

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 ...