Abstract: This paper presents a novel neural network-based optimization framework, NNDE, to solve the traveling salesman problem (TSP). The core idea is to use a radial basis function network (RBFN) ...

Researchers from Chiba University have developed a lightweight peer-selection algorithm that significantly reduces data propagation delays without increasing resource usage on internet of things (IoT) ...

It shows the schematic of the physics-informed neural network algorithm for pricing European options under the Heston model. The market price of risk is taken to be λ=0. Automatic differentiation is ...

TPUs are Google’s specialized ASICs built exclusively for accelerating tensor-heavy matrix multiplication used in deep learning models. TPUs use vast parallelism and matrix multiply units (MXUs) to ...

When Edsger W. Dijkstra published his algorithm in 1959, computer networks were barely a thing. The algorithm in question found the shortest path between any two nodes on a graph, with a variant ...

The Willow processor runs the first verifiable algorithm with real-world applications, marking a shift from theory to practical quantum computing. Google Quantum AI has demonstrated what it describes ...

The original version of this story appeared in Quanta Magazine. If you want to solve a tricky problem, it often helps to get organized. You might, for example, break the problem into pieces and tackle ...

When Fei-Fei Li arrived in Princeton in January 2007 as an assistant professor, she was assigned an office on the second floor of the computer science building. Her neighbor was Christiane Fellbaum.

This repository provides implementation for SNBO (Scalable Neural Network-based Blackbox Optimization) — a novel method for efficient blackbox optimization using neural networks. It also includes code ...