SGR turns adversarial shortcuts into teachers, isolating invariant subgraph rationales and delivering the first XGL framework ...

Calculations show that injecting randomness into a quantum neural network could help it determine properties of quantum ...

Abstract: In nonlinear control, barrier Lyapunov function is frequently utilized to handle state constraints; however, a feasibility condition for virtual signals often needs to be verified. In ...

Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory, forward propagation, backpropagation, and coding step by step for a hands ...

A research team from the Institute of Modern Physics of the Chinese Academy of Sciences and Lanzhou University has obtained ...

Most current autonomous driving systems rely on single-agent deep learning models or end-to-end neural networks. While ...

A team of researchers at Penn State have devised a new, streamlined approach to designing metasurfaces, a class of engineered ...

Understand Local Response Normalization (LRN) in deep learning: what it is, why it was introduced, and how it works in ...

New research shows that AI doesn’t need endless training data to start acting more like a human brain. When researchers ...

Neuroscience continually strives to unravel the intricate relationship between neural network morphology, spiking dynamics, and their resulting functional ...

The ability to analyze the brain's neural connectivity is emerging as a key foundation for brain-computer interface (BCI) technologies, such as controlling artificial limbs and enhancing human ...

Abstract: In this study, the adaptive hypersphere nearest neighbors (ASNN) method is proposed as an optimization framework to enhance the generalization performance of neural network classifiers. In ...