Efficient implementations of network science tools to facilitate research into human (semantic) memory. In its current version, the package contains several methods to infer networks from verbal fluency data, various network growth models, diverse (switcher-) random walk processes, and tools to analyze and visualize networks. To deliver maximum performance the majority of the code is written in C++. For an application see: Wulff, D. U., Hills, T., & Mata, R. (2018) doi:10.31234/osf.io/s73dp>.</p
Memory Networks are models equipped with a storage component where information can generally be writ...
Memristive devices have shown great promise to facilitate the acceleration and improve the power eff...
Networks are among the most prevalent formal representations in scientific studies, employed to depi...
The growing network density and unprecedented increase in network traffic, caused by the massively e...
The growing network density and unprecedented increase in network traffic, caused by the massively e...
The growing network density and unprecedented increase in network traffic, caused by the massively e...
Machine learning has become one of the go-to methods for solving problems in the field of networking...
International audienceNowadays, Machine Learning (ML) tools are commonly used in every area of scien...
This is the dataset of "Connectome of memristive nanowire networks through graph theory"Part of this...
This book collects research works that exploit neural networks and machine learning techniques from ...
The current internet architecture comprises of a series of data processing entities, called packet p...
Large-scale computational problems that need to be addressed in modern computers, such as deep learn...
In recent years, with the rapid development of current Internet and mobile communication technologie...
This work develops a generic software tool for simulation of the dynamics of sparse multi-terminal m...
Memristive devices have shown great promise to facilitate the acceleration and improve the power eff...
Memory Networks are models equipped with a storage component where information can generally be writ...
Memristive devices have shown great promise to facilitate the acceleration and improve the power eff...
Networks are among the most prevalent formal representations in scientific studies, employed to depi...
The growing network density and unprecedented increase in network traffic, caused by the massively e...
The growing network density and unprecedented increase in network traffic, caused by the massively e...
The growing network density and unprecedented increase in network traffic, caused by the massively e...
Machine learning has become one of the go-to methods for solving problems in the field of networking...
International audienceNowadays, Machine Learning (ML) tools are commonly used in every area of scien...
This is the dataset of "Connectome of memristive nanowire networks through graph theory"Part of this...
This book collects research works that exploit neural networks and machine learning techniques from ...
The current internet architecture comprises of a series of data processing entities, called packet p...
Large-scale computational problems that need to be addressed in modern computers, such as deep learn...
In recent years, with the rapid development of current Internet and mobile communication technologie...
This work develops a generic software tool for simulation of the dynamics of sparse multi-terminal m...
Memristive devices have shown great promise to facilitate the acceleration and improve the power eff...
Memory Networks are models equipped with a storage component where information can generally be writ...
Memristive devices have shown great promise to facilitate the acceleration and improve the power eff...
Networks are among the most prevalent formal representations in scientific studies, employed to depi...