Extrapolating dynamics with machine learning

Machine learning exciton dynamics

Learning and extrapolating a periodic function.

How people learn continuous functional relationships remains a poorly understood capacity. In this article, I argue that the mere presence of nonmonotonic extrapolation of periodic functions neither threatens existing theories of function learning nor distinguishes between them. However, I show that merely learning periodic functions is extremely difficult. It is only when stimuli are presented...

Predicting Internet Path Dynamics and Performance with Machine Learning

In this paper, we study the problem of predicting Internet path changes and path performance using traceroute measurements and machine learning models. Path changes are frequently linked to path inflation and performance degradation; therefore, predicting their occurrence is highly relevant for performance monitoring and dynamic traffic engineering. We introduce NETPerfTrace, an Internet Path T...

Comparative Analysis of Machine Learning Algorithms with Optimization Purposes

The field of optimization and machine learning are increasingly interplayed and optimization in different problems leads to the use of machine learning approaches‎. ‎Machine learning algorithms work in reasonable computational time for specific classes of problems and have important role in extracting knowledge from large amount of data‎. ‎In this paper‎, ‎a methodology has been employed to opt...

Quantum Neural Machine Learning - Backpropagation and Dynamics

The current work addresses quantum machine learning in the context of Quantum Artificial Neural Networks such that the networks’ processing is divided in two stages: the learning stage, where the network converges to a specific quantum circuit, and the backpropagation stage where the network effectively works as a self-programing quantum computing system that selects the quantum circuits to sol...