An approximation theory perspective on machine learning

Hippocampus-centered structural covariance network reorganization in Alzheimer’s disease: An individualized graph-based biomarker validated by machine learning

Weiye Lu, Qian Gong, Yuna Chen, Shijun Qiu, Jie An

A survey of the integration between machine learning and artificial intelligence techniques in software-defined networking

Ariel Łukowski, Grzegorz Papier, Robert Wójcik, Jerzy Domżał

Randomized forward mode gradient for spiking neural networks in scientific machine learning

Ruyin Wan, Qian Zhang, George Em Karniadakis

Stochastic Gradient Optimization with Model-Assisted Sampling

Jonne Pohjankukka, Jukka Heikkonen

This work addresses the problem of variance in stochastic gradient estimation for machine learning optimization. Deep learning relies on mini-batch methods such as stochastic gradient descent, which approximate full gradients but introduce noise, creating trade-offs between conve…Read more

Provable learning separation for predicting time-evolution of quantum many-body systems

Rahul Bandyopadhyay, Riccardo Molteni, Jens Eisert, Vedran Dunjko, Sofiene Jerbi

Given that quantum computers are naturally suited to simulate the behavior of quantum many-body systems, an immediate question arises: can one formulate physically motivated quantum machine learning (QML) tasks that exhibit learning separations? We address this problem by studyin…Read more

Cosmic Ray Modulation in the Heliosphere: Physical Mechanisms, Solar Cycle Variability, Numerical Modeling, Artificial Intelligence, and Space Weather Applications

Rekha Agarwal, RAJESH KUMAR MISHRA, Divyansh Mishra

Preservation copy of an article published in International Journal of Physical and Chemical Sciences. Read the full article: https://ioro.org/ijpcs/article/945378576347/945378576347. Cosmic ray modulation is one of the most fundamental processes in heliophysics, describing the te…Read more