Xanadu Develops Quantum Algorithm Closing in on Chemistry’s Toughest Challenge

Xanadu (Xanadu Quantum Technologies Inc.), a world leader in photonic quantum computing, today announced the publication of a ...
IEEE

Approximation Algorithms for Constructing Steiner Forest with Specific Material with Fixed Length

Abstract: In this paper, we consider the problem of constructing Steiner forest with specific material with fixed length. Specifically, given a weighted undirected graph, where each edge has an ...
Tech Xplore

Why reinforcement learning breaks at scale, and how a new method fixes it

From autonomous cars to video games, reinforcement learning (machine learning through interaction with environments) can have ...
The New York Times

I’m Building an Algorithm That Doesn’t Rot Your Brain

new video loaded: I’m Building an Algorithm That Doesn’t Rot Your Brain transcript “Our brains are being melted by the algorithm.” [MUSIC PLAYING] “Attention is infrastructure.” “Those algorithms are ...
Frontiers

Research on error control method for polynomial approximation based on equal amplitude oscillation theorem

1 Warwick Mathematics Institute, The University of Warwick, Coventry, United Kingdom 2 School of Computer and Information Engineering, Luoyang Institute of Science and Technology, Luoyang, China To ...
gc.cuny

Applications and algorithms for topologically correct approximations of curves

Abstract: In applied and numerical algebraic geometry, many problems are reduced to computing an approximation to a real algebraic curve. In order to elevate the results of such a computation to the ...
IEEE

A Novel Approximation Algorithm for the Shortest Vector Problem

Abstract: Finding the shortest vector in a lattice is a NP-hard problem. The best known approximation algorithm for this problem is LLL algorithm with the approximation factor of αn-1\2, α≥4\3, which ...
marktechpost

This AI Paper Proposes Approximation Decision Boundary ADBA: An AI Approach for Black-Box Adversarial Attacks

Machine learning methods, particularly deep neural networks (DNNs), are widely considered vulnerable to adversarial attacks. In image classification tasks, even tiny additive perturbations in the ...