1 code implementation • 22 Feb 2024 • Daniel Holmberg, Manu Airaksinen, Viviana Marchi, Andrea Guzzetta, Anna Kivi, Leena Haataja, Sampsa Vanhatalo, Teemu Roos
Reliable methods for the neurodevelopmental assessment of infants are essential for early detection of medical issues that may need prompt interventions.
no code implementations • 10 Jul 2023 • Ioanna Bouri, Fanni Franssila, Markku Alho, Giulia Cozzani, Ivan Zaitsev, Minna Palmroth, Teemu Roos
Topological analysis of the magnetic field in simulated plasmas allows the study of various physical phenomena in a wide range of settings.
no code implementations • 22 Mar 2021 • Aqsa Saeed Qureshi, Teemu Roos
We propose a novel ensemble-based CNN architecture where multiple CNN models, some of which are pre-trained and some are trained only on the data at hand, along with auxiliary data in the form of metadata associated with the input images, are combined using a meta-learner.
no code implementations • 6 Apr 2020 • Jussi Määttä, Viacheslav Bazaliy, Jyri Kimari, Flyura Djurabekova, Kai Nordlund, Teemu Roos
Many applications, especially in physics and other sciences, call for easily interpretable and robust machine learning techniques.
1 code implementation • 18 Oct 2019 • Ville Hyvönen, Elias Jääsaari, Teemu Roos
Both supervised and unsupervised machine learning algorithms have been used to learn partition-based index structures for approximate nearest neighbor (ANN) search.
no code implementations • 21 Aug 2019 • Peter Grünwald, Teemu Roos
This is an up-to-date introduction to and overview of the Minimum Description Length (MDL) Principle, a theory of inductive inference that can be applied to general problems in statistics, machine learning and pattern recognition.
2 code implementations • 18 Dec 2018 • Elias Jääsaari, Ville Hyvönen, Teemu Roos
Therefore, we propose an algorithm for automatically tuning the hyperparameters of indexing methods based on randomized space-partitioning trees.
1 code implementation • 8 Aug 2017 • Janne Leppä-aho, Santeri Räisänen, Xiao Yang, Teemu Roos
We propose a method for learning Markov network structures for continuous data without invoking any assumptions about the distribution of the variables.
no code implementations • 25 Feb 2016 • Janne Leppä-aho, Johan Pensar, Teemu Roos, Jukka Corander
We propose a Bayesian approximate inference method for learning the dependence structure of a Gaussian graphical model.
1 code implementation • 23 Sep 2015 • Ville Hyvönen, Teemu Pitkänen, Sotiris Tasoulis, Elias Jääsaari, Risto Tuomainen, Liang Wang, Jukka Corander, Teemu Roos
The method is straightforward to implement using sparse projections which leads to a reduced memory footprint and fast index construction.
no code implementations • 28 Jan 2014 • Andrew Barron, Teemu Roos, Kazuho Watanabe
The normalized maximized likelihood (NML) provides the minimax regret solution in universal data compression, gambling, and prediction, and it plays an essential role in the minimum description length (MDL) method of statistical modeling and estimation.