56 citations · 61 across the 5 of their papers we have counts for
5 papers
Structural perspective on constraint-based learning of Markov networks
Tuukka Korhonen, Fedor V. Fomin, Pekka Parviainen
Markov networks are probabilistic graphical models that employ undirected graphs to depict conditional independence relationships among variables. Our focus lies in constraint-base…
Inspecting class hierarchies in classification-based metric learning models
Hyeongji Kim, Pekka Parviainen, Terje Berge +1
Most classification models treat all misclassifications equally. However, different classes may be related, and these hierarchical relationships must be considered in some classifi…
Realistic mask generation for matter-wave lithography via machine learning
Johannes Fiedler, Adrià Salvador Palau, Eivind Kristen Osestad +2
Fast production of large area patterns with nanometre resolution is crucial for the established semiconductor industry and for enabling industrial-scale production of next-generati…
Exact Structure Discovery in Bayesian Networks with Less Space
Pekka Parviainen, Mikko Koivisto
The fastest known exact algorithms for scorebased structure discovery in Bayesian networks on n nodes run in time and space 2nnO(1). The usage of these algorithms is limited to net…
Partial Order MCMC for Structure Discovery in Bayesian Networks
Teppo Niinimaki, Pekka Parviainen, Mikko Koivisto
We present a new Markov chain Monte Carlo method for estimating posterior probabilities of structural features in Bayesian networks. The method draws samples from the posterior dis…