How modular structure can simplify tasks on networks

fast local community detection networks : parameterizing graph optimization by How modular structure can simplify tasks on

معرفی شبکه های عصبی پیمانه ای عمیق با ساختار فضایی-زمانی دوگانه جهت بهبود بازشناسی گفتار پیوسته فارسی

In this article, growable deep modular neural networks for continuous speech recognition are introduced. These networks can be grown to implement the spatio-temporal information of the frame sequences at their input layer as well as their labels at the output layer at the same time. The trained neural network with such double spatio-temporal association structure can learn the phonetic sequence...

How modular structure can simplify tasks on networks: parameterizing graph optimization by fast local community detection

By considering the task of finding the shortest walk through a Network, we find an algorithm for which the run time is not as O(2 n ), with n being the number of nodes, but instead scales with the number of nodes in a coarsened network. This coarsened network has a number of nodes related to the number of dense regions in the original graph. Since we exploit a form of local community detection ...

A Case Study in the Mechanical Verification of Fault Tolerance

To date, there is little evidence that modular reasoning about fault-tolerant systems can simplify the verification process in practice. We study this question using a prominent example from the fault tolerance literature: the problem of reliable broadcast in point-to-point networks opposed to crash failures of processes. The experiences from this case study show how modular specification techn...

Introducing Darwinian Networks

Darwinian networks (DNs) are introduced to simplify and clarify working with Bayesian networks (BNs). Rather than modelling the variables in a problem domain, DNs represent the probability tables in the model. The graphical manipulation of the tables then takes on a biological feel. It is shown how DNs can unify modeling and reasoning tasks into a single platform.