However, in many scenarios, nodes also have attributes that are correlated with the clustering structure. Thus, network information (edges) and node information (attributes) can be jointly leveraged to design high-performance clustering algorithms. Under a general model for the network and node attributes, this work establishes an information-theoretic criterion for the exact recovery of community labels and characterizes a phase transition determined by the Chernoff-Hellinger divergence of the model.

Neural Information Processing Systems http://nips.cc/

The shared part contains information for both domains. The exclusive parts, on the other hand, contain only factors of variation that are particular to each domain.

We show how existing inference and learning techniques can be adapted for the new language. Our experiments demonstrate that DeepProbLog supports (i) both symbolic and subsymbolic representations and inference, (ii) program induction, (iii) probabilistic (logic)programming, and(iv)(deep)learningfromexamples.

Theorem 4.2.Thefollowingholdsfor Algorithm 2: withprobabilityatleast1 , forallt T hP Pt,Ci 32 log ( 3e / ) ( C)2 t+ 1 1 , where = (C) Theempirical implementation condition allowsusCt, with specified components, 7 1: Experimentsonsyntheticdata.

Active learning is the task of using labelled data to select additional points to label, with the goal of fitting the most accurate model with a fixed budget of labelled points. In binary classification active learning is known to produce faster rates than passive learning for a broad range of settings.