20 citations · 43 across the 10 of their papers we have counts for
6 papers · 1 filter
Bayesian Dropout
Tue Herlau, Morten Mørup, Mikkel N. Schmidt
Dropout has recently emerged as a powerful and simple method for training neural networks preventing co-adaptation by stochastically omitting neurons. Dropout is currently not grou…
Completely random measures for modelling block-structured networks
Tue Herlau, Mikkel N. Schmidt, Morten Mørup
Many statistical methods for network data parameterize the edge-probability by attributing latent traits to the vertices such as block structure and assume exchangeability in the s…
Efficient inference of overlapping communities in complex networks
Bjarne Ørum Fruergaard, Tue Herlau
We discuss two views on extending existing methods for complex network modeling which we dub the communities first and the networks first view, respectively. Inspired by the networ…
Adaptive Reconfiguration Moves for Dirichlet Mixtures
Tue Herlau, Morten Mørup, Yee Whye Teh +1
Bayesian mixture models are widely applied for unsupervised learning and exploratory data analysis. Markov chain Monte Carlo based on Gibbs sampling and split-merge moves are widel…
Nonparametric Bayesian models of hierarchical structure in complex networks
Mikkel N. Schmidt, Tue Herlau, Morten Mørup
Analyzing and understanding the structure of complex relational data is important in many applications including analysis of the connectivity in the human brain. Such networks can…
The Infinite Degree Corrected Stochastic Block Model
Tue Herlau, Mikkel N. Schmidt, Morten Mørup
In Stochastic blockmodels, which are among the most prominent statistical models for cluster analysis of complex networks, clusters are defined as groups of nodes with statisticall…