Abstract: This letter presents a quantum graph-based solution that leverages a quantum circuit to improve learning efficiency, towards maximizing the sum-rate of wireless communication with fluid ...

Spiking Neural Networks (SNNs) offer transformative, event-driven neuromorphic computing with unparalleled energy efficiency, representing a third-generation AI paradigm. Extending this paradigm to ...

Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...

Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...

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 ...

Graph Neural Networks (GNNs) and GraphRAG don’t “reason”—they navigate complex, open-world financial graphs with traceable, multi-hop evidence. Here’s why BFSI leaders should embrace graph-native AI ...

Abstract: Edge perturbation is a basic method to modify graph structures. It can be categorized into two veins based on their effects on the performance of graph neural networks (GNNs), i.e., graph ...

Neural networks power today’s AI boom. To understand them, all we need is a map, a cat and a few thousand dimensions. Look at a picture of a cat, and you’ll instantly recognize it as a cat. But try to ...

According to mathematical legend, Peter Sarnak and Noga Alon made a bet about optimal graphs in the late 1980s. They’ve now both been proved wrong. It started with a bet. In the late 1980s, at a ...

A distinguishing feature of the neural network models used in Physics and Chemistry is that they must obey basic underlying symmetries, such as symmetry to translations, rotations, and the exchange of ...