Probabilistic programming is a powerful means for formally specifying machine learning models.The inference engine of a probabilistic programming environment can be used for serving complex queries on these models.Mos...Probabilistic programming is a powerful means for formally specifying machine learning models.The inference engine of a probabilistic programming environment can be used for serving complex queries on these models.Most of the current research in probabilistic programming is dedicated to the design and implementation of highly efficient inference engines.Much less research aims at making the power of these inference engines accessible to non-expert users.Probabilistic programming means writing code.Yet many potential users from promising application areas such as the social sciences lack programming skills.This prompted recent efforts in synthesizing probabilistic programs directly from data.However,working with synthesized programs still requires the user to read,understand,and write some code,for instance,when invoking the inference engine for answering queries.Here,we present an interactive visual approach to synthesizing and querying probabilistic programs that does not require the user to read or write code.展开更多
By skeptics and undecided we refer to nodes in clustered social networks that cannot be assigned easily to any of the clusters.Such nodes are typically found either at the interface between clusters(the undecided)or a...By skeptics and undecided we refer to nodes in clustered social networks that cannot be assigned easily to any of the clusters.Such nodes are typically found either at the interface between clusters(the undecided)or at their boundaries(the skeptics).Identifying these nodes is relevant in marketing applications like voter targeting,because the persons represented by such nodes are often more likely to be affected in marketing campaigns than nodes deeply within clusters.So far this identification task is not as well studied as other network analysis tasks like clustering,identifying central nodes,and detecting motifs.We approach this task by deriving novel geometric features from the network structure that naturally lend themselves to an interactive visual approach for identifying interface and boundary nodes.展开更多
基金This work was supported by the Carl Zeiss Foundation,Germany within the projects"Interactive Inference"and"A virtual Werkstatt for digitization in the sciences",and by the Ministry for Economics,Sciences and Digital Society of Thuringia(TMWWDG),Germany under the framework of the Landesprogramm ProDigital(DigLeben-5575/10-9).
文摘Probabilistic programming is a powerful means for formally specifying machine learning models.The inference engine of a probabilistic programming environment can be used for serving complex queries on these models.Most of the current research in probabilistic programming is dedicated to the design and implementation of highly efficient inference engines.Much less research aims at making the power of these inference engines accessible to non-expert users.Probabilistic programming means writing code.Yet many potential users from promising application areas such as the social sciences lack programming skills.This prompted recent efforts in synthesizing probabilistic programs directly from data.However,working with synthesized programs still requires the user to read,understand,and write some code,for instance,when invoking the inference engine for answering queries.Here,we present an interactive visual approach to synthesizing and querying probabilistic programs that does not require the user to read or write code.
文摘By skeptics and undecided we refer to nodes in clustered social networks that cannot be assigned easily to any of the clusters.Such nodes are typically found either at the interface between clusters(the undecided)or at their boundaries(the skeptics).Identifying these nodes is relevant in marketing applications like voter targeting,because the persons represented by such nodes are often more likely to be affected in marketing campaigns than nodes deeply within clusters.So far this identification task is not as well studied as other network analysis tasks like clustering,identifying central nodes,and detecting motifs.We approach this task by deriving novel geometric features from the network structure that naturally lend themselves to an interactive visual approach for identifying interface and boundary nodes.
