Aapo Johannes Hyvärinen / Ansvarig forskare

Forskningsresultat

Forskningsresultat per år 1999 2015 2016 2017 2019 2024

• 25 Artikel
• 15 Konferensbidrag
• 2 Kapitel

Forskningsresultat per år

Forskningsresultat per år

Sökresultat

• 2024

  Improving source estimation of retinotopic MEG responses by combining data from multiple subjects

  Hietala, P., Kurki, I. J. A., Hyvärinen, A. J. & Henriksson, L., 2024, I: Imaging Neuroscience. 2, s. 1-15 15 s.

  Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

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• 2023

  Unsupervised representation learning of spontaneous MEG data with nonlinear ICA

  Zhu, Y., Parviainen, T., Heinilä, E., Parkkonen, L. & Hyvärinen, A., 1 juli 2023, I: NeuroImage. 274, 13 s.

  Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

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• 2020

  Nonlinear ICA of fMRI reveals primitive temporal structures linked to rest, task, and behavioral traits

  Morioka, H., Calhoun, V. & Hyvarinen, A., sep. 2020, I: NeuroImage. 218, 17 s., 116989.

  Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

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• 2019

  Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA

  Monti, R. P., Zhang, K. & Hyvärinen, A., 2019, Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2019, Tel Aviv, Israel, July 22-25, 2019. AUAI Press / Association for Uncertainty in Artificial Intelligence, 10 s. (Conference on Uncertainty in Artificial Intelligence).

  Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

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• Estimation of Non-Normalized Mixture Models and Clustering Using Deep Representation

  Matsuda, T. & Hyvärinen, A., 2019, The 22nd International Conference on Artificial Intelligence and Statistics, 16-18 April 2019. Chaudhuri, K. & Sugiyama , M. (red.). Cambridge, MA: Journal of Machine Learning Research, s. 2555-2563 9 s. (Proceedings of Machine Learning Research; vol. 89).

  Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

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• Neural Empirical Bayes

  Saremi, S. & Hyvärinen, A., 2019, I: Journal of Machine Learning Research. 20, 23 s., 181.

  Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

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• Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning

  Hyvärinen, A., Sasaki, H. & Turner, R. E., 2019, The 22nd International Conference on Artificial Intelligence and Statistics, 16-18 April 2019. Chaudhuri, K. & Sugiyama , M. (red.). Cambridge, MA: Journal of Machine Learning Research, s. 859-868 10 s. (Proceedings of Machine Learning Research ; vol. 89).

  Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

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• Self-supervised representation learning from electroencephalography signals

  Banville, H., Albuquerque, I., Hyvärinen, A., Moffat, G., Engemann, D.-A. & Gramfort, A., 2019, 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, Vol. 2019. 6 s.

  Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

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• 2018

  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. (red.). Oregon: AUAI Press, s. 300-309 10 s.

  Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

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• 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, I: Journal of Machine Learning Research. 18, 47 s., 1.

  Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

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• 2017

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

  Hosoya, H. & Hyvärinen, A., juli 2017, I: PLoS Computational Biology. 13, 7, 27 s., 1005667.

  Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

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• Collaborative roles of Temporoparietal Junction and Dorsolateral Prefrontal Cortex in Different Types of Behavioural Flexibility

  Tei, S., Fujino, J., Kawada, R., Jankowski, K. F., Kauppi, J.-P., van den Bos, W., Abe, N., Sugihara, G., Miyata, J., Murai, T. & Takahashi, H., 25 juli 2017, I: Scientific Reports. 7, 8 s., 6415.

  Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

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• Functional Brain Segmentation Using Inter-Subject Correlation in fMRI

  Kauppi, J.-P., Pajula, J., Niemi, J., Hari, R. & Tohka, J., maj 2017, I: Human Brain Mapping. 38, 5, s. 2643-2665 23 s.

  Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

• Nonlinear ICA of Temporally Dependent Stationary Sources

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

  Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

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• Prediction of active peak force using a multilayer perceptron

  Niemelä, M., Kulmala, J.-P., Kauppi, J.-P., Kosonen, J. & Äyrämö, S., sep. 2017, I: Sports Engineering. 20, 3, s. 213-219

  Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

• Simultaneous Estimation of Nongaussian Components and Their Correlation Structure

  Sasaki, H., Gutmann, M. U., Shouno, H. & Hyvärinen, A., nov. 2017, I: Neural Computation. 29, 11, s. 2887-2924 38 s.

  Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

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• 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. (red.). International Machine Learning Society (IMLS), s. 2351-2362 12 s. (Proceedings of Machine Learning Research; vol. 70).

  Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

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• 2016

  Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis

  Hirayama, J.-I., Hyvarinen, A., Kiviniemi, V., Kawanabe, M. & Yamashita, O., 21 dec. 2016, I: PLoS One. 11, 12, 24 s., 0168180.

  Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

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• Disambiguating the role of noise correlations when decoding neural populations together

  Eyherabide, H. G., 2016, (Insänt) I: Neural Networks.

  Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

• Learning Visual Spatial Pooling by Strong PCA Dimension Reduction

  Hosoya, H. & Hyvärinen, A., juli 2016, I: Neural Computation. 28, 7, s. 1249-1264 16 s.

  Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

• 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. (red.). 2016 red. Hoboken, New Jersey : John Wiley, s. 185-201 17 s. (Wiley series in probability and statistics).

  Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Kapitel › Vetenskaplig › Peer review

• Orthogonal Connectivity Factorization: Interpretable Decomposition of Variability in Correlation Matrices

  Hyvärinen, A., Hirayama, J.-I., Kiviniemi, V. & Kawanabe, M., mars 2016, I: Neural Computation. 28, 3, s. 445-484 40 s.

  Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

• Sparse and low-rank matrix regularization for learning time-varying Markov networks

  Hirayama, J.-I., Hyvarinen, A. & Ishii, S., dec. 2016, I: Machine Learning. 105, 3, s. 335-366 32 s.

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• Spectators' Aesthetic Experience of Sound and Movement in Dance Performance: A Transdisciplinary Investigation

  Reason, M., Jola, C., Kay, R., Reynolds, D., Kauppi, J.-P., Grobras, M.-H., Tohka, J. & Pollick, F. E., feb. 2016, I: Psychology of Aesthetics, Creativity and the Arts. 10, 1, s. 42-55 14 s.

  Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

• Template optimization and transfer in perceptual learning

  Kurki, I., Hyvärinen, A. & Saarinen, J., aug. 2016, I: Journal of Vision. 16, 10, 18 s., 16.

  Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

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• 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. (red.). Neural Information Processing Systems Foundation, s. 3772-3780 9 s. (Advances in neural information processing systems; vol. 29, nr. NIPS 2016).

  Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review
• 2015

  A unified probabilistic model for independent and principal component analysis

  Hyvärinen, A. J., 2015, Advances in Independent Component Analysis and Learning Machines. Bingham, E., Kaski, S., Laaksonen, J. & Lampinen, J. (red.). Amsterdam: Elsevier Scientific Publ. Co, s. 75-82 8 s.

  Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Kapitel › Vetenskaplig

• Differences in fMRI intersubject correlation while viewing unedited and edited videos of dance performance

  Herbec, A., Kauppi, J.-P., Jola, C., Tohka, J. & Pollick, F. E., okt. 2015, I: Cortex. 71, s. 341-348 8 s.

  Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

• Three-Way Analysis of Spectrospatial Electromyography Data: Classification and Interpretation

  Kauppi, J.-P., Hahne, J., Müller, K.-R. & Müller, K.-R., 3 juni 2015, I: PLoS One. 10, 6, 17 s., 0127231.

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• Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals

  Kauppi, J.-P., Kandemir, M., Saarinen, V.-M., Hirvenkari, L., Parkkonen, L., Klami, A., Hari, R. & Kaski, S., 15 maj 2015, I: NeuroImage. 112, s. 288-298 11 s.

  Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

• Unifying Blind Separation and Clustering for Resting-State EEG/MEG Functional Connectivity Analysis

  Hirayama, J.-I., Ogawa, T. & Hyvärinen, A., juli 2015, I: Neural Computation. 27, 7, s. 1373-1404 32 s.

  Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review
• 2014

  A versatile software package for inter-subject correlation based analyses of fMRI

  Kauppi, J.-P., Pajula, J. & Tohka, J., 31 jan. 2014, I: Frontiers in neuroinformatics. 8, 13 s., 2.

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• Dynamic Connectivity Factorization: Interpretable decompositions of non-stationarity

  Hyvärinen, A., Hirayama, J. & Kawanabe, M., 2014, 2014 International Workshop on Pattern Recognition in Neuroimaging PRNI 2014: Proceedings, 4-6 June 2014 Tubingen, Germany. IEEE, 4 s.

  Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

• Group-PCA for very large fMRI datasets

  Smith, S. M., Hyvärinen, A., Varoquaux, G., Miller, K. L. & Beckmann, C. F., 1 nov. 2014, I: NeuroImage. 101, s. 738-749 12 s.

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• Investigating shape perception by classification images

  Kurki, I., Saarinen, J. & Hyvarinen, A., 2014, I: Journal of Vision. 14, 12, 19 s., 24.

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• 2012

  Estimation of causal orders in a linear non-gaussian acyclic model: a method robust against latent confounders

  Tashiro, T., Shimizu, S., Hyvärinen, A. & Washio, T., 2012, Artificial Neural Networks and Machine Learning – ICANN 2012: 22nd International Conference on Artificial Neural Networks, Lausanne, Switzerland, September 11-14, 2012, Proceedings. Vol. 1. s. 491-498 8 s. (Lecture Notes in Computer Science; vol. 7552).

  Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review
• 2011

  DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation model

  Shimizu, S., Inazumi, T., Sogawa, Y., Hyvärinen, A., Kawahara, Y., Washio, T., Hoyer, P. & Bollen, K., 2011, I: Journal of Machine Learning Research. 12, 24 s.

  Forskningsoutput: Tidskriftsbidrag › Artikel › Vetenskaplig › Peer review

• Hermite Polynomials and Measures of Non-Gaussianity

  Puuronen, J. S. & Hyvärinen, A., 2011, Proc. Int. Conf. on Artificial Neural Networks (ICANN2011). s. 205--212

  Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

• 2010

  A Family of Computationally Efficient and Simple Estimators for Unnormalized Statistical Models

  Pihlaja, M., Gutmann, M. U. & Hyvärinen, A. J., 2010, Conference on Uncertainty in Artificial Intelligence. 8 s.

  Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

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• 2004

  Independent Component Analysis of Word Contexts and Comparison with Traditional Categories

  Väyrynen, J. J., Honkela, T. & Hyvärinen, A., 1 juni 2004, Unknown host publication. s. 300-303 4 s.

  Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

• Linguistic Feature Extraction using Independent Component Analysis

  Honkela, T. & Hyvärinen, A., 1 juli 2004, Unknown host publication. s. 279-284 6 s.

  Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

• 1999

  Emotional Disorders in Autonomous Agents?

  Hyvärinen, A. & Honkela, T., 1999, Unknown host publication. s. 350-354 5 s.

  Forskningsoutput: Kapitel i bok/rapport/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review