Aapo Hyvärinen

226 papers A* 10A 22B 13C 6Misc 2Journal 119Unranked 47

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	A noise-corrected Langevin algorithm and sampling by half-denoising. Trans. Mach. Learn. Res. Aapo Hyvärinen
2025	A*	`conf`	Density Ratio Estimation with Conditional Probability Paths. ICML Hanlin Yu, Arto Klami, Aapo Hyvärinen, Anna Korba, Omar Chehab
2025	J	`jnl`	Density Ratio Estimation with Conditional Probability Paths. CoRR Hanlin Yu, Arto Klami, Aapo Hyvärinen, Anna Korba, Omar Chehab
2025	J	`jnl`	Identifiable Multi-View Causal Discovery Without Non-Gaussianity. CoRR Ambroise Heurtebise, Omar Chehab, Pierre Ablin, Alexandre Gramfort, Aapo Hyvärinen
2024	J	`jnl`	A noise-corrected Langevin algorithm and sampling by half-denoising. CoRR Aapo Hyvärinen
2024	A*	`conf`	Causal Representation Learning Made Identifiable by Grouping of Observational Variables. ICML Hiroshi Morioka, Aapo Hyvärinen
2024	A	`conf`	Identifiable Feature Learning for Spatial Data with Nonlinear ICA. AISTATS Hermanni Hälvä, Jonathan So, Richard E. Turner, Aapo Hyvärinen
2023	J	`jnl`	Causal Representation Learning Made Identifiable by Grouping of Observational Variables. CoRR Hiroshi Morioka, Aapo Hyvärinen
2023	A	`conf`	Connectivity-contrastive learning: Combining causal discovery and representation learning for multimodal data. AISTATS Hiroshi Morioka, Aapo Hyvärinen
2023	J	`jnl`	Identifiability of latent-variable and structural-equation models: from linear to nonlinear. CoRR Aapo Hyvärinen, Ilyes Khemakhem, Ricardo Pio Monti
2023	J	`jnl`	Identifiable Feature Learning for Spatial Data with Nonlinear ICA. CoRR Hermanni Hälvä, Jonathan So, Richard E. Turner, Aapo Hyvärinen
2023	J	`jnl`	Nonlinear Independent Component Analysis for Principled Disentanglement in Unsupervised Deep Learning. CoRR Aapo Hyvärinen, Ilyes Khemakhem, Hiroshi Morioka
2023	J	`jnl`	Nonlinear independent component analysis for principled disentanglement in unsupervised deep learning. Patterns Aapo Hyvärinen, Ilyes Khemakhem, Hiroshi Morioka
2023	J	`jnl`	Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive Estimation. CoRR Omar Chehab, Alexandre Gramfort, Aapo Hyvärinen
2023	A*	`conf`	Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond. NeurIPS Omar Chehab, Aapo Hyvärinen, Andrej Risteski
2023	J	`jnl`	Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond. CoRR Omar Chehab, Aapo Hyvärinen, Andrej Risteski
2023	J	`jnl`	Unsupervised representation learning of spontaneous MEG data with nonlinear ICA. NeuroImage Yongjie Zhu, Tiina Parviainen, Erkka Heinilä, Lauri Parkkonen, Aapo Hyvärinen
2022	A	`conf`	Binary independent component analysis: a non-stationarity-based approach. UAI Antti Hyttinen, Vitória Barin Pacela, Aapo Hyvärinen
2022	J	`jnl`	Dynamics of retinotopic spatial attention revealed by multifocal MEG. NeuroImage Ilmari Kurki, Aapo Hyvärinen, Linda Henriksson
2022	J	`jnl`	Painful intelligence: What AI can tell us about human suffering. CoRR Aapo Hyvärinen
2022	J	`jnl`	The Optimal Noise in Noise-Contrastive Learning Is Not What You Think. CoRR Omar Chehab, Alexandre Gramfort, Aapo Hyvärinen
2022	A	`conf`	The optimal noise in noise-contrastive learning is not what you think. UAI Omar Chehab, Alexandre Gramfort, Aapo Hyvärinen
2021	J	`jnl`	Adaptive Multi-View ICA: Estimation of noise levels for optimal inference. CoRR Hugo Richard, Pierre Ablin, Aapo Hyvärinen, Alexandre Gramfort, Bertrand Thirion
2021	J	`jnl`	Binary Independent Component Analysis via Non-stationarity. CoRR Antti Hyttinen, Vitória Barin Pacela, Aapo Hyvärinen
2021	A	`conf`	Causal Autoregressive Flows. AISTATS Ilyes Khemakhem, Ricardo Pio Monti, Robert Leech, Aapo Hyvärinen
2021	J	`jnl`	Direction Matters: On Influence-Preserving Graph Summarization and Max-Cut Principle for Directed Graphs. Neural Comput. Wenkai Xu, Gang Niu, Aapo Hyvärinen, Masashi Sugiyama
2021	A*	`conf`	Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA. NeurIPS Hermanni Hälvä, Sylvain Le Corff, Luc Lehéricy, Jonathan So, Yongjie Zhu, Elisabeth Gassiat, Aapo Hyvärinen
2021	J	`jnl`	Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA. CoRR Hermanni Hälvä, Sylvain Le Corff, Luc Lehéricy, Jonathan So, Yongjie Zhu, Elisabeth Gassiat, Aapo Hyvärinen
2021	A	`conf`	Independent Innovation Analysis for Nonlinear Vector Autoregressive Process. AISTATS Hiroshi Morioka, Hermanni Hälvä, Aapo Hyvärinen
2021	J	`jnl`	Information criteria for non-normalized models. J. Mach. Learn. Res. Takeru Matsuda, Masatoshi Uehara, Aapo Hyvärinen
2021	—	`conf`	Learning with self-supervision on EEG data. BCI Alexandre Gramfort, Hubert J. Banville, Omar Chehab, Aapo Hyvärinen, Denis A. Engemann
2021	A*	`conf`	Shared Independent Component Analysis for Multi-Subject Neuroimaging. NeurIPS Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, Aapo Hyvärinen
2021	J	`jnl`	Shared Independent Component Analysis for Multi-Subject Neuroimaging. CoRR Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, Aapo Hyvärinen
2020	J	`jnl`	Autoregressive flow-based causal discovery and inference. CoRR Ricardo Pio Monti, Ilyes Khemakhem, Aapo Hyvärinen
2020	J	`jnl`	Brain activity reflects the predictability of word sequences in listened continuous speech. NeuroImage Miika Koskinen, Mikko Kurimo, Joachim Gross, Aapo Hyvärinen, Riitta Hari
2020	J	`jnl`	Causal Autoregressive Flows. CoRR Ilyes Khemakhem, Ricardo Pio Monti, Robert Leech, Aapo Hyvärinen
2020	A	`conf`	Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series. UAI Hermanni Hälvä, Aapo Hyvärinen
2020	J	`jnl`	Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series. CoRR Hermanni Hälvä, Aapo Hyvärinen
2020	A*	`conf`	ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA. NeurIPS Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma, Aapo Hyvärinen
2020	J	`jnl`	ICE-BeeM: Identifiable Conditional Energy-Based Deep Models. CoRR Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma, Aapo Hyvärinen
2020	J	`jnl`	Independent innovation analysis for nonlinear vector autoregressive process. CoRR Hiroshi Morioka, Aapo Hyvärinen
2020	J	`jnl`	Modeling Shared Responses in Neuroimaging Studies through MultiView ICA. CoRR Hugo Richard, Luigi Gresele, Aapo Hyvärinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin
2020	A*	`conf`	Modeling Shared responses in Neuroimaging Studies through MultiView ICA. NeurIPS Hugo Richard, Luigi Gresele, Aapo Hyvärinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin
2020	J	`jnl`	Nonlinear ICA of fMRI reveals primitive temporal structures linked to rest, task, and behavioral traits. NeuroImage Hiroshi Morioka, Vince D. Calhoun, Aapo Hyvärinen
2020	—	`conf`	Preface: The 2020 ACM SIGKDD Workshop on Causal Discovery. CD@KDD Thuc Duy Le, Lin Liu, Kun Zhang, Emre Kiciman, Peng Cui, Aapo Hyvärinen
2020	—	`ed.`	Proceedings of the 2020 KDD Workshop on Causal Discovery (CD@KDD 2020), San Diego, CA, USA, 24 August 2020. CD@KDD Thuc Duy Le, Lin Liu, Kun Zhang, Emre Kiciman, Peng Cui, Aapo Hyvärinen
2020	A*	`conf`	Relative gradient optimization of the Jacobian term in unsupervised deep learning. NeurIPS Luigi Gresele, Giancarlo Fissore, Adrián Javaloy, Bernhard Schölkopf, Aapo Hyvärinen
2020	J	`jnl`	Relative gradient optimization of the Jacobian term in unsupervised deep learning. CoRR Luigi Gresele, Giancarlo Fissore, Adrián Javaloy, Bernhard Schölkopf, Aapo Hyvärinen
2020	A	`conf`	Robust contrastive learning and nonlinear ICA in the presence of outliers. UAI Hiroaki Sasaki, Takashi Takenouchi, Ricardo Pio Monti, Aapo Hyvärinen
2020	J	`jnl`	Uncovering the structure of clinical EEG signals with self-supervised learning. CoRR Hubert J. Banville, Omar Chehab, Aapo Hyvärinen, Denis-Alexander Engemann, Alexandre Gramfort
2020	A	`conf`	Variational Autoencoders and Nonlinear ICA: A Unifying Framework. AISTATS Ilyes Khemakhem, Diederik P. Kingma, Ricardo Pio Monti, Aapo Hyvärinen
2019	J	`jnl`	Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA. CoRR Ricardo Pio Monti, Kun Zhang, Aapo Hyvärinen
2019	A	`conf`	Causal Discovery with General Non-Linear Relationships using Non-Linear ICA. UAI Ricardo Pio Monti, Kun Zhang, Aapo Hyvärinen
2019	J	`jnl`	Decoding attentional states for neurofeedback: Mindfulness vs. wandering thoughts. NeuroImage Alexander Y. Zhigalov, Erkka Heinilä, Tiina Parviainen, Lauri Parkkonen, Aapo Hyvärinen
2019	J	`jnl`	Direction Matters: On Influence-Preserving Graph Summarization and Max-cut Principle for Directed Graphs. CoRR Wenkai Xu, Gang Niu, Aapo Hyvärinen, Masashi Sugiyama
2019	A	`conf`	Estimation of Non-Normalized Mixture Models. AISTATS Takeru Matsuda, Aapo Hyvärinen
2019	J	`jnl`	Information criteria for non-normalized models. CoRR Takeru Matsuda, Masatoshi Uehara, Aapo Hyvärinen
2019	J	`jnl`	Neural Empirical Bayes. CoRR Saeed Saremi, Aapo Hyvärinen
2019	J	`jnl`	Neural Empirical Bayes. J. Mach. Learn. Res. Saeed Saremi, Aapo Hyvärinen
2019	A	`conf`	Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning. AISTATS Aapo Hyvärinen, Hiroaki Sasaki, Richard E. Turner
2019	—	`conf`	Preface: The 2019 ACM SIGKDD Workshop on Causal Discovery. CD@KDD Thuc Duy Le, Jiuyong Li, Kun Zhang, Emre Kiciman, Peng Cui, Aapo Hyvärinen
2019	—	`ed.`	Proceedings of the 2019 ACM SIGKDD Workshop on Causal Discovery, CD@KDD 2019, Anchorage, Alaska, USA, August 5, 2019 CD@KDD Thuc Duy Le, Jiuyong Li, Kun Zhang, Emre Kiciman, Peng Cui, Aapo Hyvärinen
2019	J	`jnl`	Robust contrastive learning and nonlinear ICA in the presence of outliers. CoRR Hiroaki Sasaki, Takashi Takenouchi, Ricardo Pio Monti, Aapo Hyvärinen
2019	—	`conf`	Self-Supervised Representation Learning from Electroencephalography Signals. MLSP Hubert J. Banville, Graeme Moffat, Isabela Albuquerque, Denis-Alexander Engemann, Aapo Hyvärinen, Alexandre Gramfort
2019	J	`jnl`	Self-supervised representation learning from electroencephalography signals. CoRR Hubert J. Banville, Isabela Albuquerque, Aapo Hyvärinen, Graeme Moffat, Denis-Alexander Engemann, Alexandre Gramfort
2019	J	`jnl`	Variational Autoencoders and Nonlinear ICA: A Unifying Framework. CoRR Ilyes Khemakhem, Diederik P. Kingma, Aapo Hyvärinen
2018	J	`jnl`	A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data. CoRR Ricardo Pio Monti, Aapo Hyvärinen
2018	A	`conf`	A unified probabilistic model for learning latent factors and their connectivities from high-dimensional data . UAI Ricardo Pio Monti, Aapo Hyvärinen
2018	J	`jnl`	Deep Energy Estimator Networks. CoRR Saeed Saremi, Arash Mehrjou, Bernhard Schölkopf, Aapo Hyvärinen
2018	J	`jnl`	Estimation of Non-Normalized Mixture Models and Clustering Using Deep Representation. CoRR Takeru Matsuda, Aapo Hyvärinen
2018	J	`jnl`	Neural-Kernelized Conditional Density Estimation. CoRR Hiroaki Sasaki, Aapo Hyvärinen
2018	J	`jnl`	Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning. CoRR Aapo Hyvärinen, Hiroaki Sasaki, Richard E. Turner
2018	—	`conf`	Preface: The 2018 ACM SIGKDD Workshop on Causal Discovery. CD@KDD Thuc Duy Le, Kun Zhang, Emre Kiciman, Aapo Hyvärinen, Lin Liu
2018	—	`ed.`	Proceedings of 2018 ACM SIGKDD Workshop on Causal Discovery, CD@KDD 2018, London, UK, 20 August 2018. CD@KDD Thuc Duy Le, Kun Zhang, Emre Kiciman, Aapo Hyvärinen, Lin Liu
2017	J	`jnl`	A mixture of sparse coding models explaining properties of face neurons related to holistic and parts-based processing. PLoS Comput. Biol. Haruo Hosoya, Aapo Hyvärinen
2017	—	`conf`	Decoding emotional valence from electroencephalographic rhythmic activity. EMBC Hande Çelikkanat, Hiroki Moriya, Takeshi Ogawa, Jukka-Pekka Kauppi, Motoaki Kawanabe, Aapo Hyvärinen
2017	J	`jnl`	Density Estimation in Infinite Dimensional Exponential Families. J. Mach. Learn. Res. Bharath K. Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyvärinen, Revant Kumar
2017	J	`jnl`	Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios. J. Mach. Learn. Res. Hiroaki Sasaki, Takafumi Kanamori, Aapo Hyvärinen, Gang Niu, Masashi Sugiyama
2017	A	`conf`	Nonlinear ICA of Temporally Dependent Stationary Sources. AISTATS Aapo Hyvärinen, Hiroshi Morioka
2017	A*	`conf`	SPLICE: Fully Tractable Hierarchical Extension of ICA with Pooling. ICML Junichiro Hirayama, Aapo Hyvärinen, Motoaki Kawanabe
2017	J	`jnl`	Simultaneous Estimation of Nongaussian Components and Their Correlation Structure. Neural Comput. Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvärinen
2016	J	`jnl`	Learning Visual Spatial Pooling by Strong PCA Dimension Reduction. Neural Comput. Haruo Hosoya, Aapo Hyvärinen
2016	J	`jnl`	Orthogonal Connectivity Factorization: Interpretable Decomposition of Variability in Correlation Matrices. Neural Comput. Aapo Hyvärinen, Junichiro Hirayama, Vesa Kiviniemi, Motoaki Kawanabe
2016	J	`jnl`	Sparse and low-rank matrix regularization for learning time-varying Markov networks. Mach. Learn. Junichiro Hirayama, Aapo Hyvärinen, Shin Ishii
2016	—	`conf`	Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA. NIPS Aapo Hyvärinen, Hiroshi Morioka
2016	J	`jnl`	Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA. CoRR Aapo Hyvärinen, Hiroshi Morioka
2015	B	`conf`	Independent component analysis with an inverse problem motivated penalty term. IJCNN Jouni Puuronen, Aapo Hyvärinen
2015	J	`jnl`	Unifying Blind Separation and Clustering for Resting-State EEG/MEG Functional Connectivity Analysis. Neural Comput. Junichiro Hirayama, Takeshi Ogawa, Aapo Hyvärinen
2014	J	`jnl`	A Bayesian inverse solution using independent component analysis. Neural Networks Jouni Puuronen, Aapo Hyvärinen
2014	J	`jnl`	A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model. CoRR Shohei Shimizu, Aapo Hyvärinen, Yoshinobu Kawahara
2014	—	`conf`	Clustering via Mode Seeking by Direct Estimation of the Gradient of a Log-Density. ECML/PKDD (3) Hiroaki Sasaki, Aapo Hyvärinen, Masashi Sugiyama
2014	—	`conf`	Dynamic connectivity factorization: Interpretable decompositions of non-stationarity. PRNI Aapo Hyvärinen, Junichiro Hirayama, Motoaki Kawanabe
2014	A	`conf`	Estimating Dependency Structures for non-Gaussian Components with Linear and Energy Correlations. AISTATS Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvärinen
2014	J	`jnl`	Group-PCA for very large fMRI datasets. NeuroImage Stephen M. Smith, Aapo Hyvärinen, Gaël Varoquaux, Karla L. Miller, Christian F. Beckmann
2014	J	`jnl`	Group-level spatial independent component analysis of Fourier envelopes of resting-state MEG data. NeuroImage Pavan Ramkumar, Lauri Parkkonen, Aapo Hyvärinen
2014	—	`ch.`	Independent Component Analysis of Images. Encyclopedia of Computational Neuroscience Aapo Hyvärinen
2014	J	`jnl`	ParceLiNGAM: A Causal Ordering Method Robust Against Latent Confounders. Neural Comput. Tatsuya Tashiro, Shohei Shimizu, Aapo Hyvärinen, Takashi Washio
2014	—	`conf`	Simultaneous blind separation and clustering of coactivated EEG/MEG sources for analyzing spontaneous brain activity. EMBC Junichiro Hirayama, Takeshi Ogawa, Aapo Hyvärinen
2014	—	`ch.`	Topographic Independent Component Analysis. Encyclopedia of Computational Neuroscience Aapo Hyvärinen
2013	J	`jnl`	Bridging Information Criteria and Parameter Shrinkage for Model Selection. CoRR Kun Zhang, Heng Peng, Laiwan Chan, Aapo Hyvärinen
2013	J	`jnl`	Correlated topographic analysis: estimating an ordering of correlated components. Mach. Learn. Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvärinen
2013	J	`jnl`	Decoding magnetoencephalographic rhythmic activity using spectrospatial information. NeuroImage Jukka-Pekka Kauppi, Lauri Parkkonen, Riitta Hari, Aapo Hyvärinen
2013	J	`jnl`	Pairwise likelihood ratios for estimation of non-Gaussian structural equation models. J. Mach. Learn. Res. Aapo Hyvärinen, Stephen M. Smith
2012	J	`jnl`	A Family of Computationally Efficient and Simple Estimators for Unnormalized Statistical Models CoRR Miika Pihlaja, Michael Gutmann, Aapo Hyvärinen
2012	J	`jnl`	Causal discovery of linear acyclic models with arbitrary distributions CoRR Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph D. Ramsey, Gustavo Lacerda, Shohei Shimizu
2012	J	`jnl`	Discovery of non-gaussian linear causal models using ICA CoRR Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Patrik O. Hoyer
2012	—	`conf`	Estimation of Causal Orders in a Linear Non-Gaussian Acyclic Model: A Method Robust against Latent Confounders. ICANN (1) Tatsuya Tashiro, Shohei Shimizu, Aapo Hyvärinen, Takashi Washio
2012	B	`conf`	Learning a selectivity-invariance-selectivity feature extraction architecture for images. ICPR Michael Gutmann, Aapo Hyvärinen
2012	J	`jnl`	Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics. J. Mach. Learn. Res. Michael Gutmann, Aapo Hyvärinen
2012	J	`jnl`	On the Identifiability of the Post-Nonlinear Causal Model CoRR Kun Zhang, Aapo Hyvärinen
2012	J	`jnl`	Source Separation and Higher-Order Causal Analysis of MEG and EEG CoRR Kun Zhang, Aapo Hyvärinen
2012	C	`conf`	Topographic Analysis of Correlated Components. ACML Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvärinen
2011	C	`conf`	A General Linear Non-Gaussian State-Space Model. ACML Kun Zhang, Aapo Hyvärinen
2011	—	`conf`	Complex-Valued Independent Component Analysis of Natural Images. ICANN (2) Valero Laparra, Michael Gutmann, Jesús Malo, Aapo Hyvärinen
2011	J	`jnl`	DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model. J. Mach. Learn. Res. Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa, Aapo Hyvärinen, Yoshinobu Kawahara, Takashi Washio, Patrik O. Hoyer, Kenneth Bollen
2011	J	`jnl`	Estimating exogenous variables in data with more variables than observations. Neural Networks Yasuhiro Sogawa, Shohei Shimizu, Teppei Shimamura, Aapo Hyvärinen, Takashi Washio, Seiya Imoto
2011	—	`conf`	Extracting Coactivated Features from Multiple Data Sets. ICANN (1) Michael Gutmann, Aapo Hyvärinen
2011	—	`conf`	Hermite Polynomials and Measures of Non-gaussianity. ICANN (2) Jouni Puuronen, Aapo Hyvärinen
2011	—	`conf`	Structural equations and divisive normalization for energy-dependent component analysis. NIPS Junichiro Hirayama, Aapo Hyvärinen
2011	J	`jnl`	Testing the ICA mixing matrix based on inter-subject or inter-session consistency. NeuroImage Aapo Hyvärinen
2010	A	`conf`	A Family of Computationally E cient and Simple Estimators for Unnormalized Statistical Models. UAI Miika Pihlaja, Michael Gutmann, Aapo Hyvärinen
2010	J	`jnl`	A Two-Layer Model of Natural Stimuli Estimated with Score Matching. Neural Comput. Urs Köster, Aapo Hyvärinen
2010	—	`conf`	Discovery of Exogenous Variables in Data with More Variables Than Observations. ICANN (1) Yasuhiro Sogawa, Shohei Shimizu, Aapo Hyvärinen, Takashi Washio, Teppei Shimamura, Seiya Imoto
2010	J	`jnl`	Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity. J. Mach. Learn. Res. Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, Patrik O. Hoyer
2010	J	`jnl`	Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis. NeuroImage Aapo Hyvärinen, Pavan Ramkumar, Lauri Parkkonen, Riitta Hari
2010	A	`conf`	Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. AISTATS Michael Gutmann, Aapo Hyvärinen
2010	—	`conf`	Nonlinear acyclic causal models. NIPS Causality: Objectives and Assessment Kun Zhang, Aapo Hyvärinen
2010	C	`conf`	Pairwise Measures of Causal Direction in Linear Non-Gaussian Acyclic Models. ACML Aapo Hyvärinen
2010	A	`conf`	Source Separation and Higher-Order Causal Analysis of MEG and EEG. UAI Kun Zhang, Aapo Hyvärinen
2010	—	`conf`	Sparse and Low-Rank Estimation of Time-Varying Markov Networks with Alternating Direction Method of Multipliers. ICONIP (1) Junichiro Hirayama, Aapo Hyvärinen, Shin Ishii
2010	J	`jnl`	Statistical Models of Natural Images and Cortical Visual Representation. Top. Cogn. Sci. Aapo Hyvärinen
2010	J	`jnl`	WordICA - emergence of linguistic representations for words by independent component analysis. Nat. Lang. Eng. Timo Honkela, Aapo Hyvärinen, Jaakko J. Väyrynen
2009	A	`conf`	A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model. UAI Shohei Shimizu, Aapo Hyvärinen, Yoshinobu Kawahara
2009	—	`conf`	Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective. ECML/PKDD (2) Kun Zhang, Aapo Hyvärinen
2009	—	`conf`	Estimating Markov Random Field Potentials for Natural Images. ICA Urs Köster, Jussi T. Lindgren, Aapo Hyvärinen
2009	J	`jnl`	Estimation of linear non-Gaussian acyclic models for latent factors. Neurocomputing Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärinen
2009	—	`conf`	ICA with Sparse Connections: Revisited. ICA Kun Zhang, Heng Peng, Laiwan Chan, Aapo Hyvärinen
2009	—	`conf`	Learning Features by Contrasting Natural Images with Noise. ICANN (2) Michael Gutmann, Aapo Hyvärinen
2009	—	`conf`	Learning Natural Image Structure with a Horizontal Product Model. ICA Urs Köster, Jussi T. Lindgren, Michael Gutmann, Aapo Hyvärinen
2009	B	`conf`	Learning reconstruction and prediction of natural stimuli by a population of spiking neurons. ESANN Michael Gutmann, Aapo Hyvärinen
2009	—	`conf`	Modelling Image Complexity by Independent Component Analysis, with Application to Content-Based Image Retrieval. ICANN (2) Jukka Perkiö, Aapo Hyvärinen
2009	—	`book`	Natural Image Statistics - A Probabilistic Approach to Early Computational Vision Aapo Hyvärinen, Jarmo Hurri, Patrik O. Hoyer
2009	A	`conf`	On the Identifiability of the Post-Nonlinear Causal Model. UAI Kun Zhang, Aapo Hyvärinen
2008	A	`conf`	Causal discovery of linear acyclic models with arbitrary distributions. UAI Patrik O. Hoyer, Aapo Hyvärinen, Richard Scheines, Peter Spirtes, Joseph D. Ramsey, Gustavo Lacerda, Shohei Shimizu
2008	A*	`conf`	Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity. ICML Aapo Hyvärinen, Shohei Shimizu, Patrik O. Hoyer
2008	B	`conf`	Learning encoding and decoding filters for data representation with a spiking neuron. IJCNN Michael Gutmann, Aapo Hyvärinen, Kazuyuki Aihara
2008	B	`conf`	On the learning of nonlinear visual features from natural images by optimizing response energies. IJCNN Jussi T. Lindgren, Aapo Hyvärinen
2008	J	`jnl`	Optimal Approximation of Signal Priors. Neural Comput. Aapo Hyvärinen
2008	B	`conf`	Unsupervised learning of dependencies between local luminance and contrast in natural images. IJCNN Jussi T. Lindgren, Jarmo Hurri, Aapo Hyvärinen
2007	—	`conf`	A Two-Layer ICA-Like Model Estimated by Score Matching. ICANN (2) Urs Köster, Aapo Hyvärinen
2007	J	`jnl`	Connections Between Score Matching, Contrastive Divergence, and Pseudolikelihood for Continuous-Valued Variables. IEEE Trans. Neural Networks Aapo Hyvärinen
2007	—	`conf`	Discovery of Linear Non-Gaussian Acyclic Models in the Presence of Latent Classes. ICONIP (1) Shohei Shimizu, Aapo Hyvärinen
2007	J	`jnl`	Equivalence of Some Common Linear Feature Extraction Techniques for Appearance-Based Object Recognition Tasks. IEEE Trans. Pattern Anal. Mach. Intell. Maria Asunción Vicente, Patrik O. Hoyer, Aapo Hyvärinen
2007	J	`jnl`	Some extensions of score matching. Comput. Stat. Data Anal. Aapo Hyvärinen
2007	—	`conf`	The Statistical Properties of Local Log-Contrast in Natural Images. SCIA Jussi T. Lindgren, Jarmo Hurri, Aapo Hyvärinen
2006	J	`jnl`	A Linear Non-Gaussian Acyclic Model for Causal Discovery. J. Mach. Learn. Res. Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärinen, Antti J. Kerminen
2006	—	`conf`	A Quasi-stochastic Gradient Algorithm for Variance-Dependent Component Analysis. ICANN (2) Aapo Hyvärinen, Shohei Shimizu
2006	J	`jnl`	Consistency of Pseudolikelihood Estimation of Fully Visible Boltzmann Machines. Neural Comput. Aapo Hyvärinen
2006	—	`conf`	Emergence of conjunctive visual features by quadratic independent component analysis. NIPS Jussi T. Lindgren, Aapo Hyvärinen
2006	B	`conf`	FastISA: A fast fixed-point algorithm for independent subspace analysis. ESANN Aapo Hyvärinen, Urs Köster
2006	J	`jnl`	Finding a causal ordering via independent component analysis. Comput. Stat. Data Anal. Shohei Shimizu, Aapo Hyvärinen, Patrik O. Hoyer, Yutaka Kano
2006	B	`conf`	Learning to Segment Any Random Vector. IJCNN Aapo Hyvärinen, Jukka Perkiö
2006	—	`conf`	New Permutation Algorithms for Causal Discovery Using ICA. ICA Patrik O. Hoyer, Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Antti J. Kerminen
2006	—	`conf`	Testing Significance of Mixing and Demixing Coefficients in ICA. ICA Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Patrik O. Hoyer, Antti J. Kerminen
2005	J	`jnl`	A unifying model for blind separation of independent sources. Signal Process. Aapo Hyvärinen
2005	A	`conf`	Discovery of Non-gaussian Linear Causal Models using ICA. UAI Shohei Shimizu, Aapo Hyvärinen, Yutaka Kano, Patrik O. Hoyer
2005	J	`jnl`	Estimation of Non-Normalized Statistical Models by Score Matching. J. Mach. Learn. Res. Aapo Hyvärinen
2005	J	`jnl`	Independent component analysis of fMRI group studies by self-organizing clustering. NeuroImage Fabrizio Esposito, Tommaso Scarabino, Aapo Hyvärinen, Johan Himberg, Elia Formisano, Silvia Comani, Gioacchino Tedeschi, Rainer Goebel, Erich Seifritz, Francesco Di Salle
2004	J	`jnl`	A unifying framework for natural image statistics: spatiotemporal activity bubbles. Neurocomputing Aapo Hyvärinen, Jarmo Hurri, Jaakko J. Väyrynen
2004	J	`jnl`	Blind separation of sources that have spatiotemporal variance dependencies. Signal Process. Aapo Hyvärinen, Jarmo Hurri
2004	—	`conf`	Learning High-level Independent Components of Images through a Spectral Representation. ICPR (2) Jussi T. Lindgren, Aapo Hyvärinen
2004	B	`conf`	Linguistic feature extraction using independent component analysis. IJCNN Timo Honkela, Aapo Hyvärinen
2004	J	`jnl`	Spatiotemporal receptive fields maximizing temporal coherence in natural image sequences. Neurocomputing Jarmo Hurri, Jaakko J. Väyrynen, Aapo Hyvärinen
2004	J	`jnl`	Validating the independent components of neuroimaging time series via clustering and visualization. NeuroImage Johan Himberg, Aapo Hyvärinen, Fabrizio Esposito
2003	J	`jnl`	A two-layer temporal generative model of natural video exhibits complex-cell-like pooling of simple cell outputs. Neurocomputing Jarmo Hurri, Aapo Hyvärinen
2003	J	`jnl`	Connection between multilayer perceptrons and regression using independent component analysis. Neurocomputing Aapo Hyvärinen, Ella Bingham
2003	—	`conf`	Icasso: software for investigating the reliability of ICA estimates by clustering and visualization. NNSP Johan Himberg, Aapo Hyvärinen
2003	J	`jnl`	Independent component analysis of nondeterministic fMRI signal sources. NeuroImage Vesa Kiviniemi, Juha-Heikki Kantola, Jukka Jauhiainen, Aapo Hyvärinen, Osmo Tervonen
2003	J	`jnl`	Simple-Cell-Like Receptive Fields Maximize Temporal Coherence in Natural Video. Neural Comput. Jarmo Hurri, Aapo Hyvärinen
2002	J	`jnl`	An alternative approach to infomax and independent component analysis. Neurocomputing Aapo Hyvärinen
2002	J	`jnl`	Blind signal separation and independent component analysis. Neurocomputing Shun-ichi Amari, Aapo Hyvärinen, Soo-Young Lee, Te-Won Lee, V. David Sánchez A.
2002	J	`jnl`	Estimating Overcomplete Independent Component Bases for Image Windows. J. Math. Imaging Vis. Aapo Hyvärinen, Mika Inki
2002	J	`jnl`	Imposing sparsity on the mixing matrix in independent component analysis. Neurocomputing Aapo Hyvärinen, Karthikesh Raju
2002	—	`conf`	Interpreting Neural Response Variability as Monte Carlo Sampling of the Posterior. NIPS Patrik O. Hoyer, Aapo Hyvärinen
2002	J	`jnl`	Realizations of quantum computing using optical manipulations of atoms. Nat. Comput. Aapo Hyvärinen
2002	C	`conf`	Receptive Fields Similar to Simple Cells Maximize Temporal Coherence in Natural Video. ICANN Jarmo Hurri, Aapo Hyvärinen
2002	J	`jnl`	Sparse coding of natural contours. Neurocomputing Patrik O. Hoyer, Aapo Hyvärinen
2002	—	`conf`	Temporal Coherence, Natural Image Sequences, and the Visual Cortex. NIPS Jarmo Hurri, Aapo Hyvärinen
2001	J	`jnl`	Blind source separation by nonstationarity of variance: a cumulant-based approach. IEEE Trans. Neural Networks Aapo Hyvärinen
2001	J	`jnl`	Complexity Pursuit: Separating Interesting Components from Time Series. Neural Comput. Aapo Hyvärinen
2001	—	`book`	Independent Component Analysis Aapo Hyvärinen, Juha Karhunen, Erkki Oja
2001	J	`jnl`	Topographic Independent Component Analysis. Neural Comput. Aapo Hyvärinen, Patrik O. Hoyer, Mika Inki
2001	J	`jnl`	Topographic independent component analysis as a model of V1 organization and receptive fields. Neurocomputing Aapo Hyvärinen, Patrik O. Hoyer
2000	J	`jnl`	A Fast Fixed-Point Algorithm for Independent Component Analysis of Complex Valued Signals. Int. J. Neural Syst. Ella Bingham, Aapo Hyvärinen
2000	J	`jnl`	Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces. Neural Comput. Aapo Hyvärinen, Patrik O. Hoyer
2000	—	`conf`	Fast and robust deflationary separation of complex valued signals. EUSIPCO Ella Bingham, Aapo Hyvärinen
2000	—	`conf`	Feature Extraction from Color and Stereo Images Using ICA. IJCNN (3) Patrik O. Hoyer, Aapo Hyvärinen
2000	—	`conf`	ICA of Complex Valued Signals: A Fast and Robust Deflationary Algorithm. IJCNN (3) Ella Bingham, Aapo Hyvärinen
2000	J	`jnl`	Independent component analysis: algorithms and applications. Neural Networks Aapo Hyvärinen, Erkki Oja
2000	—	`conf`	Topographic ICA as a Model of Natural Image Statistics. Biologically Motivated Computer Vision Aapo Hyvärinen, Patrik O. Hoyer, Mika Inki
2000	—	`conf`	Topographic ICA as a Model of V1 Receptive Fields. IJCNN (4) Aapo Hyvärinen, Patrik O. Hoyer, Mika Inki
1999	B	`conf`	A fast algorithm for estimating overcomplete ICA bases for image windows. IJCNN Aapo Hyvärinen, Razvan Cristescu, Erkki Oja
1999	—	`conf`	Emergence of Topography and Complex Cell Properties from Natural Images using Extensions of ICA. NIPS Aapo Hyvärinen, Patrik O. Hoyer
1999	—	`conf`	Emotional Disorders in Autonomous Agents? ECAL Aapo Hyvärinen, Timo Honkela
1999	B	`conf`	Estimating signal-adapted wavelets using sparseness criteria. IJCNN Patrik O. Hoyer, Aapo Hyvärinen
1999	—	`conf`	Fast ICA for noisy data using Gaussian moments. ISCAS (5) Aapo Hyvärinen
1999	J	`jnl`	Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Networks Aapo Hyvärinen
1999	J	`jnl`	Gaussian moments for noisy independent component analysis. IEEE Signal Process. Lett. Aapo Hyvärinen
1999	J	`jnl`	Image Feature Extraction and Denoising by Sparse Coding. Pattern Anal. Appl. Erkki Oja, Aapo Hyvärinen, Patrik O. Hoyer
1999	B	`conf`	Independent subspace analysis shows emergence of phase and shift invariant features from natural images. IJCNN Aapo Hyvärinen, Patrik O. Hoyer
1999	J	`jnl`	Nonlinear independent component analysis: Existence and uniqueness results. Neural Networks Aapo Hyvärinen, Petteri Pajunen
1999	J	`jnl`	Sparse Code Shrinkage: Denoising of Nongaussian Data by Maximum Likelihood Estimation. Neural Comput. Aapo Hyvärinen
1999	J	`jnl`	The Fixed-Point Algorithm and Maximum Likelihood Estimation for Independent Component Analysis. Neural Process. Lett. Aapo Hyvärinen
1998	B	`conf`	Image feature extraction by sparse coding and independent component analysis. ICPR Aapo Hyvärinen, Erkki Oja, Patrik O. Hoyer, Jarmo Hurri
1998	J	`jnl`	Independent component analysis by general nonlinear Hebbian-like learning rules. Signal Process. Aapo Hyvärinen, Erkki Oja
1998	J	`jnl`	Independent component analysis in the presence of Gaussian noise by maximizing joint likelihood. Neurocomputing Aapo Hyvärinen
1998	—	`conf`	Sparse Code Shrinkage: Denoising by Nonlinear Maximum Likelihood Estimation. NIPS Aapo Hyvärinen, Patrik O. Hoyer, Erkki Oja
1997	J	`jnl`	A Fast Fixed-Point Algorithm for Independent Component Analysis. Neural Comput. Aapo Hyvärinen, Erkki Oja
1997	Misc	`conf`	A family of fixed-point algorithms for independent component analysis. ICASSP Aapo Hyvärinen
1997	Misc	`conf`	Applications of neural blind separation to signal and image processing. ICASSP Juha Karhunen, Aapo Hyvärinen, Ricardo Vigário, Jarmo Hurri, Erkki Oja
1997	C	`conf`	From Neural Principal Components to Neural Independent Components. ICANN Erkki Oja, Juha Karhunen, Aapo Hyvärinen
1997	—	`conf`	New Approximations of Differential Entropy for Independent Component Analysis and Projection Pursuit. NIPS Aapo Hyvärinen
1996	—	`conf`	A neuron that learns to separate one signal from a mixture of independent sources. ICNN Aapo Hyvärinen, Erkki Oja
1996	—	`conf`	One-unit Learning Rules for Independent Component Analysis. NIPS Aapo Hyvärinen, Erkki Oja
1996	C	`conf`	Purely Logical Neural Principal Component and Independent Component Learning. ICANN Aapo Hyvärinen
1996	J	`jnl`	Simple Neuron Models for Independent Component Analysis. Int. J. Neural Syst. Aapo Hyvärinen, Erkki Oja