Exploiting sparsity and sharing in probabilistic sensor data models

Probabilistic sensor models defined as dynamic Bayesian networks can possess an inherent sparsity that is not reflected in the structure of the network. Classical inference algorithms like variable elimination and junction tree propagation cannot exploit this sparsity. Also, they do not exploit the opportunities for sharing calculations among different time slices of the model. We show that, using a relational representation, inference expressions for these sensor models can be rewritten to make efficient use of sparsity and sharing

Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective

Neural-symbolic computing has now become the subject of interest of both academic and industry research laboratories. Graph Neural Networks (GNN) have been widely used in relational and symbolic domains, with widespread application of GNNs in combinatorial optimization, constraint satisfaction, relational reasoning and other scientific domains. The need for improved explainability, interpretability and trust of AI systems in general demands principled methodologies, as suggested by neural-symbolic computing. In this paper, we review the state-of-the-art on the use of GNNs as a model of neural-symbolic computing. This includes the application of GNNs in several domains as well as its relationship to current developments in neural-symbolic computing.Comment: Updated version, draft of accepted IJCAI2020 Survey Pape

SOFTWARE ENTREPRENEURSHIP: KNOWLEDGE NETWORKS AND PERFORMANCE OF SOFTWARE VENTURES IN CHINA AND RUSSIA

This study examines the impact of entrepreneurs’ network structure and knowledge homogeneity/heterogeneity of their network members on product development, and revenue growth of software ventures in China and Russia. The empirical data are composed of structured interviews with 159 software entrepreneurs in Beijing and Moscow. The study found that structural holes and knowledge heterogeneity affect positively product diversity in interactive ways. The study also found that knowledge homogeneity accelerates product development. Product development speed enhances revenue growth in the long term. However, the combination of speed with dense and homogeneous networks harms revenue growth over time. The effects of structural holes and knowledge heterogeneity on product diversity and revenue growth over time are more salient in Russia due to the unique institutional, social, and cultural conditions present in the country.http://deepblue.lib.umich.edu/bitstream/2027.42/40137/3/wp751.pd