REGE_NAP_001 Osztrák hadsereg gyalogosa és dobosa 1805-ből Méretarány: 54mm Tervezte: Bódi Krisztián

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We introduced REGE, an approach for improving graph embedding functions by incorporating both data and model uncertainty. Through eigen-decomposition and the student-teacher model, REGE systematically measures and utilizes data and model uncertainty, improving the robustness of graph embedding functions against adversarial attacks.

The proposed model employs residual GCN blocks and feed-forward networks to alleviate over-smoothing and over-fitting. Moreover, residual connections and patch dividing enable the capture of global and detailed spatial-temporal features.

A spatial-temporal residual graph convolutional network (GCN) for Electrocardiogram (ECG). - zhc1503/ST-ReGE

REGE, REGE.for - Classical REGE or REGGE, as also implemented in Ucinet. Similarities in terms of regular equivalence are computed. The REGE.for is a wrapper for calling the FORTRAN subrutine written by White (1985a), modified to be called by R. The REGE does the same, however it is written in R.

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1993年12月1日 · In this paper we present two algorithms for computing the extent of regular equivalence among pairs of nodes in a network. The first algorithm, REGE, is well known, but has not previously been described in the literature. The second algorithm, CATREGE, is new. Whereas REGE is applicable to quantitative data, CATREGE is used for categorical data.