default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDAapo Hyvärinen
Person information
- affiliation: University of Helsinki, Finland
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[c98]Hermanni Hälvä, Jonathan So, Richard E. Turner, Aapo Hyvärinen:
Identifiable Feature Learning for Spatial Data with Nonlinear ICA. AISTATS 2024: 3331-3339- [c97]Hiroshi Morioka, Aapo Hyvärinen:
Causal Representation Learning Made Identifiable by Grouping of Observational Variables. ICML 2024 - 2023
- [j75]Yongjie Zhu
, Tiina Parviainen, Erkka Heinilä, Lauri Parkkonen, Aapo Hyvärinen:
Unsupervised representation learning of spontaneous MEG data with nonlinear ICA. NeuroImage 274: 120142 (2023) - [j74]Aapo Hyvärinen, Ilyes Khemakhem, Hiroshi Morioka:
Nonlinear independent component analysis for principled disentanglement in unsupervised deep learning. Patterns 4(10): 100844 (2023) - [c96]Hiroshi Morioka, Aapo Hyvärinen:
Connectivity-contrastive learning: Combining causal discovery and representation learning for multimodal data. AISTATS 2023: 3399-3426 - [c95]Omar Chehab, Aapo Hyvärinen, Andrej Risteski:
Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond. NeurIPS 2023 - [i40]Omar Chehab, Alexandre Gramfort, Aapo Hyvärinen:
Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive Estimation. CoRR abs/2301.09696 (2023) - [i39]Aapo Hyvärinen, Ilyes Khemakhem, Ricardo Pio Monti:
Identifiability of latent-variable and structural-equation models: from linear to nonlinear. CoRR abs/2302.02672 (2023) - [i38]Aapo Hyvärinen, Ilyes Khemakhem, Hiroshi Morioka:
Nonlinear Independent Component Analysis for Principled Disentanglement in Unsupervised Deep Learning. CoRR abs/2303.16535 (2023) - [i37]Omar Chehab, Aapo Hyvärinen, Andrej Risteski:
Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond. CoRR abs/2310.03902 (2023) - [i36]Hiroshi Morioka, Aapo Hyvärinen:
Causal Representation Learning Made Identifiable by Grouping of Observational Variables. CoRR abs/2310.15709 (2023) - [i35]Hermanni Hälvä, Jonathan So, Richard E. Turner, Aapo Hyvärinen:
Identifiable Feature Learning for Spatial Data with Nonlinear ICA. CoRR abs/2311.16849 (2023) - 2022
- [j73]Ilmari Kurki, Aapo Hyvärinen, Linda Henriksson:
Dynamics of retinotopic spatial attention revealed by multifocal MEG. NeuroImage 263: 119643 (2022) - [c94]Omar Chehab, Alexandre Gramfort, Aapo Hyvärinen:
The optimal noise in noise-contrastive learning is not what you think. UAI 2022: 307-316 - [c93]Antti Hyttinen, Vitória Barin Pacela, Aapo Hyvärinen:
Binary independent component analysis: a non-stationarity-based approach. UAI 2022: 874-884 - [i34]Omar Chehab, Alexandre Gramfort, Aapo Hyvärinen:
The Optimal Noise in Noise-Contrastive Learning Is Not What You Think. CoRR abs/2203.01110 (2022) - [i33]Aapo Hyvärinen:
Painful intelligence: What AI can tell us about human suffering. CoRR abs/2205.15409 (2022) - 2021
- [j72]Takeru Matsuda, Masatoshi Uehara, Aapo Hyvärinen:
Information criteria for non-normalized models. J. Mach. Learn. Res. 22: 158:1-158:33 (2021) - [j71]Wenkai Xu, Gang Niu, Aapo Hyvärinen, Masashi Sugiyama:
Direction Matters: On Influence-Preserving Graph Summarization and Max-Cut Principle for Directed Graphs. Neural Comput. 33(8): 2128-2162 (2021) - [c92]Hiroshi Morioka, Hermanni Hälvä, Aapo Hyvärinen:
Independent Innovation Analysis for Nonlinear Vector Autoregressive Process. AISTATS 2021: 1549-1557 - [c91]Ilyes Khemakhem, Ricardo Pio Monti, Robert Leech, Aapo Hyvärinen:
Causal Autoregressive Flows. AISTATS 2021: 3520-3528 - [c90]Alexandre Gramfort, Hubert J. Banville, Omar Chehab, Aapo Hyvärinen, Denis A. Engemann:
Learning with self-supervision on EEG data. BCI 2021: 1-2 - [c89]Hermanni Hälvä, Sylvain Le Corff, Luc Lehéricy, Jonathan So, Yongjie Zhu, Elisabeth Gassiat, Aapo Hyvärinen:
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA. NeurIPS 2021: 1624-1633 - [c88]Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, Aapo Hyvärinen:
Shared Independent Component Analysis for Multi-Subject Neuroimaging. NeurIPS 2021: 29962-29971 - [i32]Hugo Richard, Pierre Ablin, Aapo Hyvärinen, Alexandre Gramfort, Bertrand Thirion:
Adaptive Multi-View ICA: Estimation of noise levels for optimal inference. CoRR abs/2102.10964 (2021) - [i31]Hermanni Hälvä, Sylvain Le Corff, Luc Lehéricy, Jonathan So, Yongjie Zhu, Elisabeth Gassiat, Aapo Hyvärinen:
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA. CoRR abs/2106.09620 (2021) - [i30]Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, Aapo Hyvärinen:
Shared Independent Component Analysis for Multi-Subject Neuroimaging. CoRR abs/2110.13502 (2021) - [i29]Antti Hyttinen, Vitória Barin Pacela, Aapo Hyvärinen:
Binary Independent Component Analysis via Non-stationarity. CoRR abs/2111.15431 (2021) - 2020
- [j70]Hiroshi Morioka, Vince D. Calhoun, Aapo Hyvärinen:
Nonlinear ICA of fMRI reveals primitive temporal structures linked to rest, task, and behavioral traits. NeuroImage 218: 116989 (2020) - [j69]Miika Koskinen, Mikko Kurimo, Joachim Gross, Aapo Hyvärinen, Riitta Hari:
Brain activity reflects the predictability of word sequences in listened continuous speech. NeuroImage 219: 116936 (2020) - [c87]Ilyes Khemakhem, Diederik P. Kingma, Ricardo Pio Monti, Aapo Hyvärinen:
Variational Autoencoders and Nonlinear ICA: A Unifying Framework. AISTATS 2020: 2207-2217 - [c86]Thuc Duy Le, Lin Liu, Kun Zhang, Emre Kiciman, Peng Cui, Aapo Hyvärinen:
Preface: The 2020 ACM SIGKDD Workshop on Causal Discovery. CD@KDD 2020: 1-3 - [c85]Luigi Gresele, Giancarlo Fissore, Adrián Javaloy, Bernhard Schölkopf, Aapo Hyvärinen:
Relative gradient optimization of the Jacobian term in unsupervised deep learning. NeurIPS 2020 - [c84]Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma, Aapo Hyvärinen:
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA. NeurIPS 2020 - [c83]Hugo Richard, Luigi Gresele, Aapo Hyvärinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin:
Modeling Shared responses in Neuroimaging Studies through MultiView ICA. NeurIPS 2020 - [c82]Hiroaki Sasaki, Takashi Takenouchi, Ricardo Pio Monti, Aapo Hyvärinen:
Robust contrastive learning and nonlinear ICA in the presence of outliers. UAI 2020: 659-668 - [c81]Hermanni Hälvä, Aapo Hyvärinen:
Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series. UAI 2020: 939-948 - [e3]Thuc Duy Le, Lin Liu, Kun Zhang, Emre Kiciman, Peng Cui, Aapo Hyvärinen:
Proceedings of the 2020 KDD Workshop on Causal Discovery (CD@KDD 2020), San Diego, CA, USA, 24 August 2020. Proceedings of Machine Learning Research 127, PMLR 2020 [contents] - [i28]Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma, Aapo Hyvärinen:
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models. CoRR abs/2002.11537 (2020) - [i27]Hugo Richard, Luigi Gresele, Aapo Hyvärinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin:
Modeling Shared Responses in Neuroimaging Studies through MultiView ICA. CoRR abs/2006.06635 (2020) - [i26]Hiroshi Morioka, Aapo Hyvärinen:
Independent innovation analysis for nonlinear vector autoregressive process. CoRR abs/2006.10944 (2020) - [i25]Hermanni Hälvä, Aapo Hyvärinen:
Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series. CoRR abs/2006.12107 (2020) - [i24]Luigi Gresele, Giancarlo Fissore, Adrián Javaloy, Bernhard Schölkopf, Aapo Hyvärinen:
Relative gradient optimization of the Jacobian term in unsupervised deep learning. CoRR abs/2006.15090 (2020) - [i23]Ricardo Pio Monti, Ilyes Khemakhem, Aapo Hyvärinen:
Autoregressive flow-based causal discovery and inference. CoRR abs/2007.09390 (2020) - [i22]Hubert J. Banville, Omar Chehab, Aapo Hyvärinen, Denis-Alexander Engemann, Alexandre Gramfort:
Uncovering the structure of clinical EEG signals with self-supervised learning. CoRR abs/2007.16104 (2020) - [i21]Ilyes Khemakhem, Ricardo Pio Monti, Robert Leech, Aapo Hyvärinen:
Causal Autoregressive Flows. CoRR abs/2011.02268 (2020)
2010 – 2019
- 2019
- [j68]Saeed Saremi, Aapo Hyvärinen:
Neural Empirical Bayes. J. Mach. Learn. Res. 20: 181:1-181:23 (2019) - [j67]Alexander Y. Zhigalov, Erkka Heinilä, Tiina Parviainen, Lauri Parkkonen, Aapo Hyvärinen:
Decoding attentional states for neurofeedback: Mindfulness vs. wandering thoughts. NeuroImage 185: 565-574 (2019) - [c80]Aapo Hyvärinen, Hiroaki Sasaki, Richard E. Turner:
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning. AISTATS 2019: 859-868 - [c79]Takeru Matsuda, Aapo Hyvärinen:
Estimation of Non-Normalized Mixture Models. AISTATS 2019: 2555-2563 - [c78]Thuc Duy Le, Jiuyong Li, Kun Zhang, Emre Kiciman, Peng Cui, Aapo Hyvärinen:
Preface: The 2019 ACM SIGKDD Workshop on Causal Discovery. CD@KDD 2019: 1-3 - [c77]Hubert J. Banville, Graeme Moffat, Isabela Albuquerque, Denis-Alexander Engemann, Aapo Hyvärinen, Alexandre Gramfort:
Self-Supervised Representation Learning from Electroencephalography Signals. MLSP 2019: 1-6 - [c76]Ricardo Pio Monti, Kun Zhang, Aapo Hyvärinen:
Causal Discovery with General Non-Linear Relationships using Non-Linear ICA. UAI 2019: 186-195 - [e2]Thuc Duy Le, Jiuyong Li, Kun Zhang, Emre Kiciman, Peng Cui, Aapo Hyvärinen:
Proceedings of the 2019 ACM SIGKDD Workshop on Causal Discovery, CD@KDD 2019, Anchorage, Alaska, USA, August 5, 2019. Proceedings of Machine Learning Research 104, PMLR 2019 [contents] - [i20]Saeed Saremi, Aapo Hyvärinen:
Neural Empirical Bayes. CoRR abs/1903.02334 (2019) - [i19]Ricardo Pio Monti, Kun Zhang, Aapo Hyvärinen:
Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA. CoRR abs/1904.09096 (2019) - [i18]Takeru Matsuda, Masatoshi Uehara, Aapo Hyvärinen:
Information criteria for non-normalized models. CoRR abs/1905.05976 (2019) - [i17]Ilyes Khemakhem, Diederik P. Kingma, Aapo Hyvärinen:
Variational Autoencoders and Nonlinear ICA: A Unifying Framework. CoRR abs/1907.04809 (2019) - [i16]Wenkai Xu, Gang Niu, Aapo Hyvärinen, Masashi Sugiyama:
Direction Matters: On Influence-Preserving Graph Summarization and Max-cut Principle for Directed Graphs. CoRR abs/1907.09588 (2019) - [i15]Hiroaki Sasaki, Takashi Takenouchi, Ricardo Pio Monti, Aapo Hyvärinen:
Robust contrastive learning and nonlinear ICA in the presence of outliers. CoRR abs/1911.00265 (2019) - [i14]Hubert J. Banville, Isabela Albuquerque, Aapo Hyvärinen, Graeme Moffat, Denis-Alexander Engemann, Alexandre Gramfort:
Self-supervised representation learning from electroencephalography signals. CoRR abs/1911.05419 (2019) - 2018
- [c75]Thuc Duy Le, Kun Zhang, Emre Kiciman, Aapo Hyvärinen, Lin Liu:
Preface: The 2018 ACM SIGKDD Workshop on Causal Discovery. CD@KDD 2018: 1-3 - [c74]Ricardo Pio Monti, Aapo Hyvärinen:
A unified probabilistic model for learning latent factors and their connectivities from high-dimensional data . UAI 2018: 300-309 - [e1]Thuc Duy Le, Kun Zhang, Emre Kiciman, Aapo Hyvärinen, Lin Liu:
Proceedings of 2018 ACM SIGKDD Workshop on Causal Discovery, CD@KDD 2018, London, UK, 20 August 2018. Proceedings of Machine Learning Research 92, PMLR 2018 [contents] - [i13]Takeru Matsuda, Aapo Hyvärinen:
Estimation of Non-Normalized Mixture Models and Clustering Using Deep Representation. CoRR abs/1805.07516 (2018) - [i12]Saeed Saremi, Arash Mehrjou, Bernhard Schölkopf, Aapo Hyvärinen:
Deep Energy Estimator Networks. CoRR abs/1805.08306 (2018) - [i11]Aapo Hyvärinen, Hiroaki Sasaki, Richard E. Turner:
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning. CoRR abs/1805.08651 (2018) - [i10]Ricardo Pio Monti, Aapo Hyvärinen:
A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data. CoRR abs/1805.09567 (2018) - [i9]Hiroaki Sasaki, Aapo Hyvärinen:
Neural-Kernelized Conditional Density Estimation. CoRR abs/1806.01754 (2018) - 2017
- [j66]Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyvärinen, Revant Kumar:
Density Estimation in Infinite Dimensional Exponential Families. J. Mach. Learn. Res. 18: 57:1-57:59 (2017) - [j65]Hiroaki Sasaki, Takafumi Kanamori, Aapo Hyvärinen, Gang Niu, Masashi Sugiyama:
Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios. J. Mach. Learn. Res. 18: 180:1-180:47 (2017) - [j64]Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvärinen:
Simultaneous Estimation of Nongaussian Components and Their Correlation Structure. Neural Comput. 29(11) (2017) - [j63]Haruo Hosoya, Aapo Hyvärinen:
A mixture of sparse coding models explaining properties of face neurons related to holistic and parts-based processing. PLoS Comput. Biol. 13(7) (2017) - [c73]Aapo Hyvärinen, Hiroshi Morioka:
Nonlinear ICA of Temporally Dependent Stationary Sources. AISTATS 2017: 460-469 - [c72]Hande Çelikkanat, Hiroki Moriya, Takeshi Ogawa, Jukka-Pekka Kauppi, Motoaki Kawanabe, Aapo Hyvärinen:
Decoding emotional valence from electroencephalographic rhythmic activity. EMBC 2017: 4143-4146 - [c71]Junichiro Hirayama, Aapo Hyvärinen, Motoaki Kawanabe:
SPLICE: Fully Tractable Hierarchical Extension of ICA with Pooling. ICML 2017: 1491-1500 - 2016
- [j62]Junichiro Hirayama, Aapo Hyvärinen, Shin Ishii:
Sparse and low-rank matrix regularization for learning time-varying Markov networks. Mach. Learn. 105(3): 335-366 (2016) - [j61]Aapo Hyvärinen, Junichiro Hirayama, Vesa Kiviniemi, Motoaki Kawanabe:
Orthogonal Connectivity Factorization: Interpretable Decomposition of Variability in Correlation Matrices. Neural Comput. 28(3): 445-484 (2016) - [j60]Haruo Hosoya, Aapo Hyvärinen:
Learning Visual Spatial Pooling by Strong PCA Dimension Reduction. Neural Comput. 28(7): 1249-1264 (2016) - [c70]Aapo Hyvärinen, Hiroshi Morioka:
Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA. NIPS 2016: 3765-3773 - [i8]Aapo Hyvärinen, Hiroshi Morioka:
Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA. CoRR abs/1605.06336 (2016) - 2015
- [j59]Junichiro Hirayama, Takeshi Ogawa, Aapo Hyvärinen:
Unifying Blind Separation and Clustering for Resting-State EEG/MEG Functional Connectivity Analysis. Neural Comput. 27(7): 1373-1404 (2015) - [c69]Jouni Puuronen, Aapo Hyvärinen:
Independent component analysis with an inverse problem motivated penalty term. IJCNN 2015: 1-7 - 2014
- [j58]Tatsuya Tashiro, Shohei Shimizu, Aapo Hyvärinen, Takashi Washio:
ParceLiNGAM: A Causal Ordering Method Robust Against Latent Confounders. Neural Comput. 26(1): 57-83 (2014) - [j57]Pavan Ramkumar, Lauri Parkkonen, Aapo Hyvärinen:
Group-level spatial independent component analysis of Fourier envelopes of resting-state MEG data. NeuroImage 86: 480-491 (2014) - [j56]Stephen M. Smith, Aapo Hyvärinen, Gaël Varoquaux, Karla L. Miller, Christian F. Beckmann:
Group-PCA for very large fMRI datasets. NeuroImage 101: 738-749 (2014) - [j55]Jouni Puuronen, Aapo Hyvärinen:
A Bayesian inverse solution using independent component analysis. Neural Networks 50: 47-59 (2014) - [c68]Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvärinen:
Estimating Dependency Structures for non-Gaussian Components with Linear and Energy Correlations. AISTATS 2014: 868-876 - [c67]Junichiro Hirayama, Takeshi Ogawa, Aapo Hyvärinen:
Simultaneous blind separation and clustering of coactivated EEG/MEG sources for analyzing spontaneous brain activity. EMBC 2014: 4932-4935 - [c66]Hiroaki Sasaki, Aapo Hyvärinen, Masashi Sugiyama:
Clustering via Mode Seeking by Direct Estimation of the Gradient of a Log-Density. ECML/PKDD (3) 2014: 19-34 - [c65]Aapo Hyvärinen, Junichiro Hirayama, Motoaki Kawanabe:
Dynamic connectivity factorization: Interpretable decompositions of non-stationarity. PRNI 2014: 1-4 - [r2]Aapo Hyvärinen:
Independent Component Analysis of Images. Encyclopedia of Computational Neuroscience 2014 - [r1]Aapo Hyvärinen:
Topographic Independent Component Analysis. Encyclopedia of Computational Neuroscience 2014 - [i7]Shohei Shimizu, Aapo Hyvärinen, Yoshinobu Kawahara:
A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model. CoRR abs/1408.2038 (2014) - 2013
- [j54]Aapo Hyvärinen, Stephen M. Smith:
Pairwise likelihood ratios for estimation of non-Gaussian structural equation models. J. Mach. Learn. Res. 14(1): 111-152 (2013) - [j53]Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvärinen:
Correlated topographic analysis: estimating an ordering of correlated components. Mach. Learn. 92(2-3): 285-317 (2013) - [j52]Jukka-Pekka Kauppi, Lauri Parkkonen, Riitta Hari, Aapo Hyvärinen:
Decoding magnetoencephalographic rhythmic activity using spectrospatial information. NeuroImage 83: 921-936 (2013) - [i6]Kun Zhang, Heng Peng, Laiwan Chan, Aapo Hyvärinen:
Bridging Information Criteria and Parameter Shrinkage for Model Selection. CoRR abs/1307.2307 (2013) - 2012
- [j51]Michael Gutmann, Aapo Hyvärinen:
Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics. J. Mach. Learn. Res. 13: 307-361 (2012) - [c64]Tatsuya Tashiro, Shohei Shimizu, Aapo Hyvärinen, Takashi Washio:
Estimation of Causal Orders in a Linear Non-Gaussian Acyclic Model: A Method Robust against Latent Confounders. ICANN (1) 2012: 491-498 - [c63]Michael Gutmann, Aapo Hyvärinen:
Learning a selectivity-invariance-selectivity feature extraction architecture for images. ICPR 2012: 918-921 - [c62]Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvärinen:
Topographic Analysis of Correlated Components. ACML 2012: 365-378 - [i5]Miika Pihlaja, Michael Gutmann, Aapo Hyvärinen:
A Family of Computationally Efficient and Simple Estimators for Unnormalized Statistical Models. CoRR abs/1203.3506 (2012) - [i4]Kun Zhang, Aapo Hyvärinen:
Source Separation and Higher-Order Causal Analysis of MEG and EEG. CoRR abs/1203.3533 (2012) - [i3]Kun Zhang, Aapo Hyvärinen:
On the Identifiability of the Post-Nonlinear Causal Model. CoRR abs/1205.2599 (2012) - [i2]Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph D. Ramsey, Gustavo Lacerda, Shohei Shimizu:
Causal discovery of linear acyclic models with arbitrary distributions. CoRR abs/1206.3260 (2012) - [i1]Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Patrik O. Hoyer:
Discovery of non-gaussian linear causal models using ICA. CoRR abs/1207.1413 (2012) - 2011
- [j50]Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa, Aapo Hyvärinen, Yoshinobu Kawahara, Takashi Washio, Patrik O. Hoyer, Kenneth Bollen:
DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model. J. Mach. Learn. Res. 12: 1225-1248 (2011) - [j49]Aapo Hyvärinen:
Testing the ICA mixing matrix based on inter-subject or inter-session consistency. NeuroImage 58(1): 122-136 (2011) - [j48]Yasuhiro Sogawa, Shohei Shimizu, Teppei Shimamura, Aapo Hyvärinen, Takashi Washio, Seiya Imoto:
Estimating exogenous variables in data with more variables than observations. Neural Networks 24(8): 875-880 (2011) - [c61]Jouni Puuronen, Aapo Hyvärinen:
Hermite Polynomials and Measures of Non-gaussianity. ICANN (2) 2011: 205-212 - [c60]Valero Laparra, Michael Gutmann, Jesús Malo, Aapo Hyvärinen:
Complex-Valued Independent Component Analysis of Natural Images. ICANN (2) 2011: 213-220 - [c59]Michael Gutmann, Aapo Hyvärinen:
Extracting Coactivated Features from Multiple Data Sets. ICANN (1) 2011: 323-330 - [c58]Junichiro Hirayama, Aapo Hyvärinen:
Structural equations and divisive normalization for energy-dependent component analysis. NIPS 2011: 1872-1880 - [c57]Kun Zhang, Aapo Hyvärinen:
A General Linear Non-Gaussian State-Space Model. ACML 2011: 113-128 - 2010
- [j47]Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, Patrik O. Hoyer:
Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity. J. Mach. Learn. Res. 11: 1709-1731 (2010) - [j46]Urs Köster, Aapo Hyvärinen:
A Two-Layer Model of Natural Stimuli Estimated with Score Matching. Neural Comput. 22(9): 2308-2333 (2010) - [j45]Aapo Hyvärinen, Pavan Ramkumar, Lauri Parkkonen, Riitta Hari:
Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis. NeuroImage 49(1): 257-271 (2010) - [j44]