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 ...

In the first half of this course, we will explore the evolution of deep neural network language models, starting with n-gram models and proceeding through feed-forward neural networks, recurrent ...

Researchers have devised a way to make computer vision systems more efficient by building networks out of computer chips’ logic gates. Networks programmed directly into computer chip hardware can ...

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 ...

Demand prediction plays a critical role in traffic research. The key challenge of traffic demand prediction lies in modeling the complex spatial dependencies and temporal dynamics. However, there is ...

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 ...

How does the brain manage to catch the drift of a mumbled sentence or a flat, robotic voice? A new study led by researchers ...