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In this work, we propose the use of two neural models performing jointly in order to minimize the same energy function. This model is focused on obtaining ...
To further consider the correlation information among items, we treat items within a basket as nodes in a vanilla graph and learn node representations via ...
In this work, we propose the use of two neural models performing jointly in order to minimize the same energy function. This model is focused on obtaining ...
In this work, we propose the use of two neural models performing jointly in order to minimize the same energy function. This model is focused on obtaining good ...
K -Pages Graph Drawing with Multivalued Neural Networks · Two Pages Graph Layout Via Recurrent Multivalued Neural Networks · Stochastic multivalued network for ...
In this paper, the K-pages graph layout problem is solved by a new neural model. This model consists of two neural networks performing jointly in order to ...
Via statistical significance tests, we determine the relevant graph(s) for each medically-derived feature. We then employ a multiple-graph recurrent graph ...
Missing: Two Layout
We propose to use multiple graphs to encode the spatial and other heterogenous inter-station correlations. The temporal dynamics of the inter- station ...
Through modeling the latent variables of graph data, GraphVRNN can capture the joint distributions of graph structures and the underlying node attributes. We ...
Introduction to Graph Neural Networks. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers, 2020. book.