We address the problem of classification in partially labeled networks (a.k.a. within-network classification) where observed class labels are sparse. Techniques for statistical re...
Brian Gallagher, Hanghang Tong, Tina Eliassi-Rad, ...
: The initialisation of a neural network implementation of Sammon's mapping, either randomly or based on the principal components (PCs) of the sample covariance matrix, is exp...
Boaz Lerner, Hugo Guterman, Mayer Aladjem, Its'hak...
Background: Structural analysis of cellular interaction networks contributes to a deeper understanding of network-wide interdependencies, causal relationships, and basic functiona...
Steffen Klamt, Julio Saez-Rodriguez, Jonathan A. L...
Abstract. We give the first exponential separation between quantum and bounded-error randomized one-way communication complexity. Specifically, we define the Hidden Matching Proble...
We present a method for explaining predictions for individual instances. The presented approach is general and can be used with all classification models that output probabilities...