Researchers trained a deep multilayer perceptron on finite element and numerical data to predict the performance of a ...

Overview:  Deep learning uses multi-layer neural networks to learn patterns from data.CNNs, RNNs, LSTMs, transformers, and autoencoders support different t ...

A biologically inspired pulvinar-like skip pathway in a convolutional vision model acts primarily as a gain controller and ...

"I use ChatGPT and AI image generators, but why is AI so smart?" "What is the difference between machine learning and deep learning anyway?"AI is a hot topic in the news and on social media every ...

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 ...

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.

Trained optical layers classify hidden features of laser beams, offering a new way to decode information carried by ...

We study deep neural networks and their use in semiparametric inference. We establish novel rates of convergence for deep feedforward neural nets. Our new rates are sufficiently fast (in some cases ...

A neural network is a machine learning (ML) model designed to process data in a way that mimics the function and structure of the human brain. Neural networks are intricate networks of interconnected ...

Called Dynamic Clustering for Channel Pruning (DCCP), the method reduced the floating-point operations of ResNet-50 by 68.52% ...