Neuroinformatics research group / Aapo Hyvärinen

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  1. SPLICE: Fully tractable hierarchical extension of ICA with pooling

    Hirayama, J., Hyvärinen, A. J. & Kawanabe, M., 2017, Proceedings of the 34 th International Conference on Machine Learning, Sydney, Australia. Precup, D. & Teh, Y. W. (eds.). International Machine Learning Society (IMLS), p. 2351-2362 12 p. (Proceedings of Machine Learning Research; vol. 70).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  2. A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data

    Monti, R. P. & Hyvärinen, A., 6 Aug 2018, Uncertainty in Artificial Intelligence: Proceedings of the Thirty-Fourth Conference (2018). Globerson, A. & Silva, R. (eds.). Oregon: AUAI Press, p. 300-309 10 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  3. Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios

    Sasaki, H., Kanamori, T., Hyvärinen, A., Niu, G. & Sugiyama, M., 2018, In : Journal of Machine Learning Research. 18, 47 p., 1.

    Research output: Contribution to journalArticleScientificpeer-review

  4. Nonlinear Functional Causal Models for Distinguishing Cause from Effect

    Hyvärinen, A. & Zhang, K., 2016, Statistics and Causality: Methods for Applied Empirical Research. Wiedermann, W. & von Eye, A. (eds.). 2016 ed. Hoboken, New Jersey : John Wiley, p. 185-201 17 p. (Wiley series in probability and statistics).

    Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

  5. Nonlinear ICA of Temporally Dependent Stationary Sources

    Hyvärinen, A. & Morioka, H., 2017, Articial Intelligence and Statistics (AISTATS 2017). Singh, A. & Zhu, J. (eds.). Microtome Publishing, p. 460-469 10 p. (Proceedings of Machine Learning Research; vol. 54).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  6. Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA

    Hyvärinen, A. & Morioka, H., 2016, Advances in Neural Information Processing Systems. Garnett, R., Lee, D. D., von Luxburg, U., Guyon, I. & Sugiyama, M. (eds.). Neural Information Processing Systems Foundation, p. 3772-3780 9 p. (Advances in neural information processing systems; vol. 29, no. NIPS 2016).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

  7. Simultaneous Estimation of Nongaussian Components and Their Correlation Structure

    Sasaki, H., Gutmann, M. U., Shouno, H. & Hyvärinen, A., Nov 2017, In : Neural Computation. 29, 11, p. 2887-2924 38 p.

    Research output: Contribution to journalArticleScientificpeer-review

  8. Prediction of active peak force using a multilayer perceptron

    Niemelä, M., Kulmala, J-P., Kauppi, J-P., Kosonen, J. & Äyrämö, S., Sep 2017, In : Sports Engineering. 20, 3, p. 213-219

    Research output: Contribution to journalArticleScientificpeer-review

  9. A mixture of sparse coding models explaining properties of face neurons related to holistic and parts-based processing

    Hosoya, H. & Hyvärinen, A., Jul 2017, In : PLoS Computational Biology. 13, 7, 27 p., 1005667.

    Research output: Contribution to journalArticleScientificpeer-review

  10. Functional Brain Segmentation Using Inter-Subject Correlation in fMRI

    Kauppi, J-P., Pajula, J., Niemi, J., Hari, R. & Tohka, J., May 2017, In : Human Brain Mapping. 38, 5, p. 2643-2665 23 p.

    Research output: Contribution to journalArticleScientificpeer-review

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