56 citations · 123 across the 10 of their papers we have counts for
10 papers
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…
Distance-Ratio-Based Formulation for Metric Learning
Hyeongji Kim, Pekka Parviainen, Ketil Malde
In metric learning, the goal is to learn an embedding so that data points with the same class are close to each other and data points with different classes are far apart. We propo…
Learning Large DAGs by Combining Continuous Optimization and Feedback Arc Set Heuristics
Pierre Gillot, Pekka Parviainen
Bayesian networks represent relations between variables using a directed acyclic graph (DAG). Learning the DAG is an NP-hard problem and exact learning algorithms are feasible only…
Measuring Adversarial Robustness using a Voronoi-Epsilon Adversary
Hyeongji Kim, Pekka Parviainen, Ketil Malde
Previous studies on robustness have argued that there is a tradeoff between accuracy and adversarial accuracy. The tradeoff can be inevitable even when we neglect generalization. W…
Distributed Bayesian Matrix Factorization with Limited Communication
Xiangju Qin, Paul Blomstedt, Eemeli Leppäaho +2
Bayesian matrix factorization (BMF) is a powerful tool for producing low-rank representations of matrices and for predicting missing values and providing confidence intervals. Scal…
Learning Structures of Bayesian Networks for Variable Groups
Pekka Parviainen, Samuel Kaski
Bayesian networks, and especially their structures, are powerful tools for representing conditional independencies and dependencies between random variables. In applications where…
Local Structure Discovery in Bayesian Networks
Teppo Niinimaki, Pekka Parviainen
Learning a Bayesian network structure from data is an NP-hard problem and thus exact algorithms are feasible only for small data sets. Therefore, network structures for larger netw…
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…