scholarly journals Robust architecture search using network adaptation

2021 ◽  
Vol 30 (5) ◽  
pp. 290-294
Author(s):  
Amrita Rana ◽  
Kyung Ki Kim
Keyword(s):  
2018 ◽  
Vol 155 ◽  
pp. 01037
Author(s):  
Sergey Gorbachev ◽  
Vladimir Syryamkin

The article is devoted to research and development of adaptive algorithms for neuro-fuzzy inference when solving multicriteria problems connected with analysis of expert (foresight) data to identify technological breakthroughs and strategic perspectives of scientific, technological and innovative development. The article describes the optimized structuralfunctional scheme of the high-performance adaptive neuro-fuzzy classifier with a logical output, which has such specific features as a block of decision tree-based fuzzy rules and a hybrid algorithm for neural network adaptation of parameters based on the error back-propagation to the root of the decision tree.


2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Navavat Pipatsart ◽  
Wannapong Triampo ◽  
Charin Modchang

We presented adaptive random network models to describe human behavioral change during epidemics and performed stochastic simulations of SIR (susceptible-infectious-recovered) epidemic models on adaptive random networks. The interplay between infectious disease dynamics and network adaptation dynamics was investigated in regard to the disease transmission and the cumulative number of infection cases. We found that the cumulative case was reduced and associated with an increasing network adaptation probability but was increased with an increasing disease transmission probability. It was found that the topological changes of the adaptive random networks were able to reduce the cumulative number of infections and also to delay the epidemic peak. Our results also suggest the existence of a critical value for the ratio of disease transmission and adaptation probabilities below which the epidemic cannot occur.


Author(s):  
Jonathan Bignell

The chapter focuses on the comedy drama Episodes (2011–2018), made by the British production company Hat Trick for the BBC and Showtime. A British husband and wife duo of screenwriters work on a US network adaptation of their hit UK comedy show, which is “Americanized,” and they fight for their creative authority and their marriage. Episodes has a hybrid identity in terms of form, format, and genre, expressed in decisions including setting, casting, and performance style. Each of these can be read as a commentary on the similarities and differences between American and British television cultures, alongside the narrative’s thematization of cultural and national differences. Episodes talks about transatlantic television and self-consciously performs it, asking whether a program or a person can be transatlantic by making a joke of it. The chapter argues that Episodes is a metacommentary on deeply embedded myths about the TV of each nation.


Author(s):  
Elisa Jimeno ◽  
Jordi Perez-Romero ◽  
Irene Vila Munoz ◽  
Begona Blanco ◽  
Aitor Sanchoyerto ◽  
...  

2020 ◽  
Vol 8 (S1) ◽  
pp. S110-S144 ◽  
Author(s):  
Jan Treur

AbstractIn network models for real-world domains, often network adaptation has to be addressed by incorporating certain network adaptation principles. In some cases, also higher order adaptation occurs: the adaptation principles themselves also change over time. To model such multilevel adaptation processes, it is useful to have some generic architecture. Such an architecture should describe and distinguish the dynamics within the network (base level), but also the dynamics of the network itself by certain adaptation principles (first-order adaptation level), and also the adaptation of these adaptation principles (second-order adaptation level), and may be still more levels of higher order adaptation. This paper introduces a multilevel network architecture for this, based on the notion network reification. Reification of a network occurs when a base network is extended by adding explicit states representing the characteristics of the structure of the base network. It will be shown how this construction can be used to explicitly represent network adaptation principles within a network. When the reified network is itself also reified, also second-order adaptation principles can be explicitly represented. The multilevel network reification construction introduced here is illustrated for an adaptive adaptation principle from social science for bonding based on homophily and one for metaplasticity in Cognitive Neuroscience.


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