Enes Causal Discovery

Enes The proposed architecture is a mixture of experts, which allows for the model entities, such as the causal relationships, to be further parameterized. More specifically, an attempt is made to exploit a neural net as implementing neurons poses a great challenge for this dataset. To explain, a simple and fast Pearson coefficient linear model usually achieves good scores. An aggressive baseline that requires a really good model to overcome that is. Moreover, there are major limitations when it comes to causal discovery of observational data. Unlike the sachs one did not use interventions but only prior knowledge; the most prohibiting limitation is that of the data which is addressed. Thereafter, the method and the model are described and after that the results are presented.

Fuzzy Hierarchical Multiplex

A new fuzzy optimization framework that extends FCM causality is proposed. This model utilizes the dynamics to map data into metrics and create a framework that examines logical implication and hierarchy of concepts using a multiplex. Moreover, this is a white-theoretical paper introducing the framework and analyzing the logic and math behind it. Upon this extension the main objectives and the orientation of this framework is expounded and exemplified; this framework is meant for service optimization of information transmission in service process design. Lastly, a thorough analysis of the FHM is included which is done following the logical steps in a simple and elegant manner.