Aapo Hyvärinen

发表

Relative gradient optimization of the Jacobian term in unsupervised deep learning pdf

Luigi Gresele, Giancarlo Fissore, Adrián Javaloy, 2020, NeurIPS.

Aapo Hyvärinen, Saeed Saremi, Saeed Saremi, 2019, J. Mach. Learn. Res..

Simultaneous blind separation and clustering of coactivated EEG/MEG sources for analyzing spontaneous brain activity

Aapo Hyvärinen, Junichiro Hirayama, Takeshi Ogawa, 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Icasso: software for investigating the reliability of ICA estimates by clustering and visualization

Aapo Hyvärinen, Johan Himberg, J. Himberg, 2003, 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718).

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

Aapo Hyvärinen, Takeru Matsuda, 2018, ArXiv.

Pattern Recognition (ICPR), 2012 21st International Conference on

Aapo Hyvärinen, Michael Gutmann, A. Hyvärinen, 2012 .

A Non-Negative Sparse Coding Network Learns Contour Coding and Integration From Natural Images

Aapo Hyvärinen, Patrik O. Hoyer, A. Hyvärinen, 2001 .

Characterization of Spontaneous Neuromagnetic Brain Rhythms Using Independent Component Analysis of Short-Time Fourier Transforms

Aapo Hyvärinen, Pavan Ramkumar, Lauri Parkkonen, 2010 .

Representation of spatial frequency in human retinotopic areas

Aapo Hyvärinen, Linda Henriksson, Lauri Nurminen, 2007 .

Image Feature Extraction Using Independent Component Analysis

Erkki Oja, Juha Karhunen, Aapo Hyvärinen, 1996 .

Discovery of Linear Non-Gaussian Acyclic Models in the Presence of Latent Classes

Aapo Hyvärinen, Shohei Shimizu, Shohei Shimizu, 2007, ICONIP.

Sparse Code Shrinkage: Denoising by Nonlinear Maximum Likelihood Estimation

Erkki Oja, Aapo Hyvärinen, Patrik O. Hoyer, 1998, NIPS.

A unifying model for blind separation of independent sources

Aapo Hyvärinen, Aapo Hyvärinen, 2005, Signal Process..

Independent component analysis in the presence of Gaussian noise by maximizing joint likelihood

Aapo Hyvärinen, A. Hyvärinen, Aapo Hyvärinen, 1998, Neurocomputing.

Survey on Independent Component Analysis

Aapo Hyvärinen, A. Hyvärinen, Aapo Hyvärinen, 1999 .

Independent component analysis by general nonlinear Hebbian-like learning rules

Erkki Oja, Aapo Hyvärinen, E. Oja, 1998, Signal Process..

Optimization Theory and Algorithms

Aapo Hyvärinen, Patrik O. Hoyer, Jarmo Hurri, 2009 .

Independent component analysis with an inverse problem motivated penalty term

Aapo Hyvärinen, Jouni Puuronen, Aapo Hyvärinen, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

A three-layer model of natural image statistics

Aapo Hyvärinen, Michael U. Gutmann, Michael U Gutmann, 2013, Journal of Physiology-Paris.

Causal Discovery with General Non-Linear Relationships using Non-Linear ICA pdf

Aapo Hyvärinen, Ricardo Pio Monti, Kun Zhang, 2019, UAI.

Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis

Aapo Hyvärinen, Lauri Parkkonen, Riitta Hari, 2010, NeuroImage.

A Two-Layer Model of Natural Stimuli Estimated with Score Matching

Aapo Hyvärinen, Urs Köster, Urs Köster, 2010, Neural Computation.

Unsupervised learning of dependencies between local luminance and contrast in natural images

Aapo Hyvärinen, Jarmo Hurri, Jussi T. Lindgren, 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

Blind separation of sources that have spatiotemporal variance dependencies

Aapo Hyvärinen, Jarmo Hurri, J. Hurri, 2004, Signal Process..

Simple-Cell-Like Receptive Fields Maximize Temporal Coherence in Natural Video

Aapo Hyvärinen, Jarmo Hurri, J. Hurri, 2003, Neural Computation.

Independent Component Analysis

Erkki Oja, Juha Karhunen, Aapo Hyvärinen, 2001 .

Sparse Code Shrinkage: Denoising of Nongaussian Data by Maximum Likelihood Estimation

Aapo Hyvärinen, A. Hyvärinen, Aapo Hyvärinen, 1999, Neural Computation.

Conclusion and Future Prospects

Aapo Hyvärinen, Patrik O. Hoyer, Jarmo Hurri, 2009 .

Statistical Models of Natural Images and Cortical Visual Representation

Aapo Hyvärinen, Aapo Hyvärinen, 2010, Top. Cogn. Sci..

Complexity Pursuit: Separating Interesting Components from Time Series

Aapo Hyvärinen, Aapo Hyvärinen, 2001, Neural Computation.

The Statistical Properties of Local Log-Contrast in Natural Images

Aapo Hyvärinen, Jarmo Hurri, Jussi T. Lindgren, 2007, SCIA.

Independent Component Analysis of Images

Aapo Hyvärinen, 2014, Encyclopedia of Computational Neuroscience.

Variational Autoencoders and Nonlinear ICA: A Unifying Framework pdf

Aapo Hyvärinen, Diederik P. Kingma, Ilyes Khemakhem, 2019, AISTATS.

Nonlinear ICA of Temporally Dependent Stationary Sources

Aapo Hyvärinen, Hiroshi Morioka, H. Morioka, 2017, AISTATS.

Complex-Valued Independent Component Analysis of Natural Images

Aapo Hyvärinen, Valero Laparra, Michael Gutmann, 2011, ICANN.

Proc. Conf. on Uncertainty in Artificial Intelligence (UAI)

Aapo Hyvärinen, Michael U. Gutmann, Miika Pihlaja, 2010 .

A unified probabilistic model for learning latent factors and their connectivities from high-dimensional data pdf

Aapo Hyvärinen, Ricardo Pio Monti, R. Monti, 2018, UAI.

Estimating Overcomplete Independent Component Bases for Image Windows

Aapo Hyvärinen, Mika Inki, A. Hyvärinen, 2002, Journal of Mathematical Imaging and Vision.

Aapo Hyvärinen, Michael Gutmann, Hayaru Shouno, 2013, Machine Learning.

Sparse coding of natural contours

Aapo Hyvärinen, Patrik O. Hoyer, P. Hoyer, 2002, Neurocomputing.

FastISA: A fast fixed-point algorithm for independent subspace analysis

Aapo Hyvärinen, Urs Köster, Urs Köster, 2006, ESANN.

Proc. Asian Conference on Machine Learning (ACML)

Aapo Hyvärinen, Michael U. Gutmann, Hayaru Shouno, 2012 .

A Two-Layer ICA-Like Model Estimated by Score Matching

Aapo Hyvärinen, Urs Köster, A. Hyvärinen, 2007, ICANN.

Representation of Cross-Frequency Spatial Phase Relationships in Human Visual Cortex

Aapo Hyvärinen, Linda Henriksson, Simo Vanni, 2009, The Journal of Neuroscience.

Decoding attentional states for neurofeedback: Mindfulness vs. wandering thoughts

Aapo Hyvärinen, Lauri Parkkonen, Alexander Zhigalov, 2019, NeuroImage.

Estimation of Causal Orders in a Linear Non-Gaussian Acyclic Model: A Method Robust against Latent Confounders

Aapo Hyvärinen, Takashi Washio, Shohei Shimizu, 2012, ICANN.

DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model

Aapo Hyvärinen, Yoshinobu Kawahara, Patrik O. Hoyer, 2011, J. Mach. Learn. Res..

Natural Image Statistics - A Probabilistic Approach to Early Computational Vision

Aapo Hyvärinen, Patrik O. Hoyer, Jarmo Hurri, 2009, Computational Imaging and Vision.

advancescomponent analysis: recent

Aapo Hyvärinen, Aapo Hyvärinen, 2013 .

Template optimization and transfer in perceptual learning.

Aapo Hyvärinen, Jussi Saarinen, Ilmari Kurki, 2016, Journal of vision.

Group-level spatial independent component analysis of Fourier envelopes of resting-state MEG data

Aapo Hyvärinen, Lauri Parkkonen, Pavan Ramkumar, 2014, NeuroImage.

Representation of broadband edges and spatial phase congruency in human visual cortex

Aapo Hyvärinen, Linda Henriksson, Simo Vanni, 2010 .

Characterising subjecto-to-subject variability of brain connectivity with constrained principal component analysis

Motoaki Kawanabe, Aapo Hyvärinen, Jun-ichiro Hirayama, 2017 .

Faculty Opinions recommendation of Parameter learning but not structure learning: a Bayesian network model of constraints on early perceptual learning.

Aapo Hyvärinen, A. Hyvärinen, 2007 .

Dynamic connectivity factorization: Interpretable decompositions of non-stationarity

Motoaki Kawanabe, Aapo Hyvärinen, Junichiro Hirayama, 2014, 2014 International Workshop on Pattern Recognition in Neuroimaging.

Faculty Opinions recommendation of Network modelling methods for FMRI.

Aapo Hyvärinen, Aapo Hyvärinen, 2010 .

From Neural Principal Components to Neural Independent Components

Erkki Oja, Juha Karhunen, Aapo Hyvärinen, 1997, ICANN.

Testing Independent Component Patterns by Inter-Subject or Inter-Session Consistency

Aapo Hyvärinen, Pavan Ramkumar, A. Hyvärinen, 2013, Front. Hum. Neurosci..

Fast and robust fixed-point algorithms for independent component analysis

Aapo Hyvärinen, A. Hyvärinen, Aapo Hyvärinen, 1999, IEEE Trans. Neural Networks.

Dependencies of Energy Detectors: Beyond V1

Aapo Hyvärinen, Patrik O. Hoyer, Jarmo Hurri, 2009 .

Topography as a property of the natural sensory world

Aapo Hyvärinen, Aapo Hyvärinen, 2002, Natural Computing.

Estimating Markov Random Field Potentials for Natural Images

Aapo Hyvärinen, Urs Köster, Jussi T. Lindgren, 2009, ICA.

The Discrete Fourier Transform

Aapo Hyvärinen, Patrik O. Hoyer, Jarmo Hurri, 2009 .

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

Aapo Hyvärinen, Vince Calhoun, Hiroshi Morioka, 2020, NeuroImage.

Brain activity reflects the predictability of word sequences in listened continuous speech

Aapo Hyvärinen, Joachim Gross, Riitta Hari, 2020, NeuroImage.

Topographic independent component analysis as a model of V1 organization and receptive fields

Aapo Hyvärinen, Patrik O. Hoyer, A. Hyvärinen, 2001, Neurocomputing.

Hermite Polynomials and Measures of Non-gaussianity

Aapo Hyvärinen, Jouni Puuronen, Aapo Hyvärinen, 2011, ICANN.

ICE-BeeM: Identifiable Conditional Energy-Based Deep Models pdf

Aapo Hyvärinen, Ricardo Pio Monti, Diederik P. Kingma, 2020, NeurIPS.

SPATIOTEMPORAL LINEAR SIMPLE-CELL MODELS BASED ON TEMPORAL COHERENCE AND INDEPENDENT COMPONENT ANALYSIS

Aapo Hyvärinen, Jarmo Hurri, Jaakko J. Väyrynen, 2004 .

A Hierarchical Statistical Model of Natural Images Explains Tuning Properties in V2

Aapo Hyvärinen, Haruo Hosoya, H. Hosoya, 2015, The Journal of Neuroscience.

A General Linear Non-Gaussian State-Space Model

Aapo Hyvärinen, Kun Zhang, Kun Zhang, 2011, ACML.

Decoding Magnetoencephalographic Rhythmic Activity Using Spectrospatial Information

Aapo Hyvärinen, Lauri Parkkonen, Riitta Hari, 2022 .

Statistical Estimation in Conceptual Spaces

Aapo Hyvärinen, 1998 .

Emergence of conjunctive visual features by quadratic independent component analysis

Aapo Hyvärinen, Jussi T. Lindgren, J. T. Lindgren, 2006, NIPS.

Spatiotemporal receptive fields maximizing temporal coherence in natural image sequences

Aapo Hyvärinen, Jarmo Hurri, Jaakko J. Väyrynen, 2004, Neurocomputing.

Bridging Information Criteria and Parameter Shrinkage for Model Selection pdf

Lai-Wan Chan, Aapo Hyvärinen, Kun Zhang, 2013, ArXiv.

Chapter 2 Independent component analysis and blind source separation

Tapani Ristaniemi, Juha Karhunen, Alexander Ilin, 2004 .

Pairwise likelihood ratios for estimation of non-Gaussian structural equation models

Aapo Hyvärinen, Stephen M. Smith, Stephen M. Smith, 2013, J. Mach. Learn. Res..

A Linear Non-Gaussian Acyclic Model for Causal Discovery

Aapo Hyvärinen, Patrik O. Hoyer, Shohei Shimizu, 2006, J. Mach. Learn. Res..

Independent Component Analysis: A Tutorial

Aapo Hyvärinen, Erkki Oja, E. Oja, 1999 .

Proc. Int. Joint Conference on Neural Networks (IJCNN)

Kazuyuki Aihara, Aapo Hyvärinen, Michael Gutmann, 2008 .

Neural Independent Component Analysis - Approaches and Applications

Erkki Oja, Juha Karhunen, Aapo Hyvärinen, 1998 .

Image Feature Extraction and Denoising by Sparse Coding

Erkki Oja, Aapo Hyvärinen, Patrik O. Hoyer, 1999, Pattern Analysis & Applications.

Self-Supervised Representation Learning from Electroencephalography Signals

Aapo Hyvärinen, Isabela Albuquerque, Alexandre Gramfort, 2019, 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP).

Emergence of Linguistic Representations by Independent Component Analysis

Aapo Hyvärinen, Timo Honkela, Jaakko J. Väyrynen, 2003 .

Topographic Independent Component Analysis

Aapo Hyvärinen, Patrik O. Hoyer, Mika Inki, 2001, Neural Computation.

Learning Topographic Representations for Linearly Correlated Components

Aapo Hyvärinen, Michael U. Gutmann, Hayaru Shouno, 2011 .

Estimation of Non-Normalized Statistical Models by Score Matching

Aapo Hyvärinen, A. Hyvärinen, Aapo Hyvärinen, 2005, J. Mach. Learn. Res..

Causal discovery of linear acyclic models with arbitrary distributions

Aapo Hyvärinen, Patrik O. Hoyer, Richard Scheines, 2008, UAI.

Feature extraction from colour and stereo images using ICA

Aapo Hyvärinen, Patrik O. Hoyer, P. Hoyer, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

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

Gang Niu, Takafumi Kanamori, Aapo Hyvärinen, 2017, J. Mach. Learn. Res..

Imposing sparsity on the mixing matrix in independent component analysis

Aapo Hyvärinen, Karthikesh Raju, K. Raju, 2002, Neurocomputing.

Independent Component Analysis for Time-dependent Stochastic Processes

Aapo Hyvärinen, Aapo Hyvärinen, 1998 .

Causal Autoregressive Flows pdf

Aapo Hyvärinen, Ricardo Pio Monti, Robert Leech, 2020, ArXiv.

A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model pdf

Aapo Hyvärinen, Yoshinobu Kawahara, Shohei Shimizu, 2009, UAI.

A two-layer temporal generative model of natural video exhibits complex-cell-like pooling of simple cell outputs

Aapo Hyvärinen, Jarmo Hurri, 2003, Neurocomputing.

Spatial dependencies between local luminance and contrast in natural images.

Aapo Hyvärinen, Jarmo Hurri, Jussi T Lindgren, 2008, Journal of vision.

A family of fixed-point algorithms for independent component analysis

Aapo Hyvärinen, Aapo Hyvärinen, 1997, ICASSP.

Using classification images to reveal the critical features in global shape perception

Aapo Hyvärinen, Jussi Saarinen, Ilmari Kurki, 2010 .

On the Identifiability of the Post-Nonlinear Causal Model

Aapo Hyvärinen, Kun Zhang, Aapo Hyvärinen, 2009, UAI.

A Novel Temporal Generative Model of Natural Video as an Internal Model in Early Vision

Aapo Hyvärinen, Jarmo Hurri, J. Hurri, 2002 .

Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS)

Aapo Hyvärinen, Michael Gutmann, A. Hyvärinen, 2010 .

Multivariate Probability and Statistics

Aapo Hyvärinen, Patrik O. Hoyer, Jarmo Hurri, 2009 .

Gaussian moments for noisy independent component analysis

Aapo Hyvärinen, A. Hyvärinen, Aapo Hyvärinen, 1999, IEEE Signal Processing Letters.

Validating the independent components of neuroimaging time series via clustering and visualization

Aapo Hyvärinen, Johan Himberg, Fabrizio Esposito, 2004, NeuroImage.

Extracting Coactivated Features from Multiple Data Sets

Aapo Hyvärinen, Michael Gutmann, 2011, ICANN.

Topographic ICA as a Model of Natural Image Statistics

Aapo Hyvärinen, Patrik O. Hoyer, Mika Inki, 2000, Biologically Motivated Computer Vision.

A unifying framework for natural image statistics: spatiotemporal activity bubbles

Aapo Hyvärinen, Jarmo Hurri, Jaakko J. Väyrynen, 2004, Neurocomputing.

Orthogonal Connectivity Factorization: Interpretable Decomposition of Variability in Correlation Matrices

Motoaki Kawanabe, Aapo Hyvärinen, Junichiro Hirayama, 2016, Neural Computation.

Nonlinear acyclic causal models

Aapo Hyvärinen, Kun Zhang, 2010, NIPS Causality: Objectives and Assessment.

Information-Theoretic Interpretations

Aapo Hyvärinen, Patrik O. Hoyer, Jarmo Hurri, 2009 .

Topographic Analysis of Correlated Components

Aapo Hyvärinen, Michael Gutmann, Hayaru Shouno, 2012, ACML.

Lateral Interactions and Feedback

Aapo Hyvärinen, Patrik O. Hoyer, Jarmo Hurri, 2009 .

Linear Filters and Frequency Analysis

Aapo Hyvärinen, Patrik O. Hoyer, Jarmo Hurri, 2009 .

WordICA - emergence of linguistic representations for words by independent component analysis

Aapo Hyvärinen, Timo Honkela, Jaakko J. Väyrynen, 2010, Nat. Lang. Eng..

Modelling Image Complexity by Independent Component Analysis, with Application to Content-Based Image Retrieval

Aapo Hyvärinen, Jukka Perkiö, Aapo Hyvärinen, 2009, ICANN.

Energy Correlations and Topographic Organization

Aapo Hyvärinen, Patrik O. Hoyer, Jarmo Hurri, 2009 .

SPLICE: Fully Tractable Hierarchical Extension of ICA with Pooling

Motoaki Kawanabe, Aapo Hyvärinen, Junichiro Hirayama, 2017, ICML.

Independent subspace analysis shows emergence of phase and shift invariant features from natural images

Aapo Hyvärinen, Patrik O. Hoyer, P. Hoyer, 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

Topographic Independent Component Analysis

Aapo Hyvärinen, A. Hyvärinen, 2001, Neural Computation.

Neural-Kernelized Conditional Density Estimation pdf

Aapo Hyvärinen, Hiroaki Sasaki, H. Sasaki, 2018, ArXiv.

Emergence of Topography and Complex Cell Properties from Natural Images using Extensions of ICA

Aapo Hyvärinen, Patrik O. Hoyer, P. Hoyer, 1999, NIPS.

Faculty Opinions recommendation of Non-Bayesian contour synthesis.

Aapo Hyvärinen, 2011 .

Connection between multilayer perceptrons and regression using independent component analysis

Aapo Hyvärinen, Ella Bingham, E. Bingham, 2003, Neurocomputing.

Proc. of the International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning (AKRR)

Aapo Hyvärinen, Patrik O. Hoyer, Michael U. Gutmann, 2005 .

Independent Component Analysis

Erkki Oja, Juha Karhunen, Aapo Hyvärinen, 2001, IEEE Transactions on Neural Networks.

Blind signal separation and independent component analysis

Aapo Hyvärinen, Shun-ichi Amari, Te-Won Lee, 2002, Neurocomputing.

Emergence of Linguistic Features: Independent Component Analysis of Contexts

Aapo Hyvärinen, Timo Honkela, Jaakko J. Väyrynen, 2005 .

Workshop on Deep Learning and Unsupervised Feature Learning, NIPS

Aapo Hyvärinen, Michael U. Gutmann, Hayaru Shouno, 2011 .

Learning reconstruction and prediction of natural stimuli by a population of spiking neurons

Aapo Hyvärinen, Michael Gutmann, 2009, ESANN.

Collinear context (and learning) change the profile of the perceptual filter

Aapo Hyvärinen, Ilmari Kurki, Pentti Laurinen, 2006, Vision Research.

Discovery of Non-gaussian Linear Causal Models using ICA

Aapo Hyvärinen, Patrik O. Hoyer, Shohei Shimizu, 2005, UAI.

Estimation of unnormalized statistical models without numerical integration

Aapo Hyvärinen, Michael U. Gutmann, A. Hyvärinen, 2013 .

Information criteria for non-normalized models pdf

Masatoshi Uehara, Aapo Hyvärinen, Takeru Matsuda, 2019, J. Mach. Learn. Res..

Crash Course on Linear Algebra

Aapo Hyvärinen, Patrik O. Hoyer, Jarmo Hurri, 2009 .

Statistical models of images and early vision

Aapo Hyvärinen, Patrik O. Hoyer, Jarmo Hurri, 2005 .

Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective

Aapo Hyvärinen, Kun Zhang, Aapo Hyvärinen, 2009, ECML/PKDD.

F1000Prime recommendation of Unsupervised natural visual experience rapidly reshapes size-invariant object representation in inferior temporal cortex.

Aapo Hyvärinen, Michael U. Gutmann, 2010 .

ParceLiNGAM: A Causal Ordering Method Robust Against Latent Confounders

Aapo Hyvärinen, Takashi Washio, Shohei Shimizu, 2013, Neural Computation.

Estimating Dependency Structures for non-Gaussian Components with Linear and Energy Correlations

Aapo Hyvärinen, Michael Gutmann, Hayaru Shouno, 2014, AISTATS.

Independent innovation analysis for nonlinear vector autoregressive process pdf

Aapo Hyvärinen, Hiroshi Morioka, 2020, ArXiv.

Fast and robust deflationary separation of complex valued signals

Aapo Hyvärinen, Ella Bingham, E. Bingham, 2000, 2000 10th European Signal Processing Conference.

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

Aapo Hyvärinen, Haruo Hosoya, 2016 .

Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis

Motoaki Kawanabe, Aapo Hyvärinen, Jun-ichiro Hirayama, 2016, PloS one.

Two Methods for Estimating Overcomplete Independent Component Bases

Aapo Hyvärinen, Mika Inki, M. Inki, 2001 .

A neuron that learns to separate one signal from a mixture of independent sources

Aapo Hyvärinen, Erkki Oja, E. Oja, 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

Learning Visual Spatial Pooling by Strong PCA Dimension Reduction

Aapo Hyvärinen, Haruo Hosoya, H. Hosoya, 2016, Neural Computation.

New Permutation Algorithms for Causal Discovery Using ICA

Aapo Hyvärinen, Patrik O. Hoyer, Shohei Shimizu, 2006, ICA.

Decoding emotional valence from electroencephalographic rhythmic activity

Motoaki Kawanabe, Aapo Hyvärinen, Takeshi Ogawa, 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

An alternative approach to infomax and independent component analysis

Aapo Hyvärinen, Aapo Hyvärinen, 2002, Neurocomputing.

Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics

Aapo Hyvärinen, Michael Gutmann, A. Hyvärinen, 2012, J. Mach. Learn. Res..

Deep Energy Estimator Networks pdf

Bernhard Schölkopf, Aapo Hyvärinen, Saeed Saremi, 2018, ArXiv.

Discovery of Exogenous Variables in Data with More Variables Than Observations

Aapo Hyvärinen, Teppei Shimamura, Takashi Washio, 2009, ICANN.

Autoregressive flow-based causal discovery and inference pdf

Aapo Hyvärinen, Ricardo Pio Monti, Ilyes Khemakhem, 2020, ArXiv.

Clustering via Mode Seeking by Direct Estimation of the Gradient of a Log-Density

Aapo Hyvärinen, Masashi Sugiyama, Hiroaki Sasaki, 2014, ECML/PKDD.

Testing Significance of Mixing and Demixing Coefficients in ICA

Aapo Hyvärinen, Patrik O. Hoyer, Shohei Shimizu, 2006, ICA.

Proc. Workshop on Information Theoretic Methods in Science and Engineering (WITMSE2013)

Aapo Hyvärinen, Michael U. Gutmann, A. Hyvärinen, 2013 .

A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images

Aapo Hyvärinen, Patrik O. Hoyer, A. Hyvärinen, 2001, Vision Research.

Principal Component Analysis and Whitening

Erkki Oja, Juha Karhunen, Aapo Hyvärinen, 2002 .

Convolutive Mixtures and Blind Deconvolution

Erkki Oja, Juha Karhunen, Aapo Hyvärinen, 2002 .

Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces

Aapo Hyvärinen, Patrik O. Hoyer, A. Hyvärinen, 2000, Neural Computation.

Noise-contrastive estimation: A new estimation principle for unnormalized statistical models

Aapo Hyvärinen, Michael Gutmann, A. Hyvärinen, 2010, AISTATS.

Interpreting Neural Response Variability as Monte Carlo Sampling of the Posterior

Aapo Hyvärinen, Patrik O. Hoyer, P. Hoyer, 2002, NIPS.

Bubbles: a unifying framework for low-level statistical properties of natural image sequences.

Aapo Hyvärinen, Jarmo Hurri, Jaakko Väyrynen, 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

Some extensions of score matching

Aapo Hyvärinen, A. Hyvärinen, Aapo Hyvärinen, 2007, Comput. Stat. Data Anal..

17th International Conference on Biomagnetism Advances in Biomagnetism, Biomag2010, Dubrovnik, Croatia, 28.3.-1.4.2010

Aapo Hyvärinen, Pavan Ramkumar, Riitta Hari, 2010 .

Fast ICA for noisy data using Gaussian moments

Aapo Hyvärinen, A. Hyvärinen, 1999, ISCAS.

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

Klaus-Robert Müller, Aapo Hyvärinen, Jukka-Pekka Kauppi, 2015, PloS one.

Group-PCA for very large fMRI datasets

Aapo Hyvärinen, Stephen M. Smith, Gaël Varoquaux, 2014, NeuroImage.

A Family of Computationally E cient and Simple Estimators for Unnormalized Statistical Models

Aapo Hyvärinen, Michael Gutmann, Miika Pihlaja, 2010, UAI.

ICA with Sparse Connections: Revisited

Lai-Wan Chan, Aapo Hyvärinen, Kun Zhang, 2009, ICA.

ICA by Minimization of Mutual Information

Juha Karhunen, Aapo Hyvärinen, Erkki Oja, 2002 .

Color and Stereo Images

Aapo Hyvärinen, Patrik O. Hoyer, Jarmo Hurri, 2009 .

Independent Component Analysis For Binary Data: An Experimental Study

Aapo Hyvärinen, Johan Himberg, J. Himberg, 2001 .

Independent component analysis of nondeterministic fMRI signal sources

Aapo Hyvärinen, Vesa Kiviniemi, Osmo Tervonen, 2003, NeuroImage.

A fast algorithm for estimating overcomplete ICA bases for image windows

Erkki Oja, Aapo Hyvärinen, Razvan Cristescu, 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

Learning Natural Image Structure with a Horizontal Product Model

Aapo Hyvärinen, Michael Gutmann, Urs Köster, 2009, ICA.

Consistency of Pseudolikelihood Estimation of Fully Visible Boltzmann Machines

Aapo Hyvärinen, A. Hyvärinen, Aapo Hyvärinen, 2006, Neural Computation.

Analysing spatiotemporal dynamics in contrast detection by Classification Images

Aapo Hyvärinen, Jussi Saarinen, Ilmari Kurki, 2010 .

Direction Matters: On Influence-Preserving Graph Summarization and Max-Cut Principle for Directed Graphs pdf

Gang Niu, Aapo Hyvärinen, Masashi Sugiyama, 2019, Neural Computation.

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

Shin Ishii, Aapo Hyvärinen, Junichiro Hirayama, 2016, Machine Learning.

Image feature extraction by sparse coding and independent component analysis

Erkki Oja, Aapo Hyvärinen, Patrik O. Hoyer, 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

Receptive Fields Similar to Simple Cells Maximize Temporal Coherence in Natural Video

Aapo Hyvärinen, Jarmo Hurri, J. Hurri, 2002, ICANN.

Learning high-level independent components of images through a spectral representation

Aapo Hyvärinen, Jussi T. Lindgren, J. T. Lindgren, 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

Interpretable brain age prediction using linear latent variable models of functional connectivity

Motoaki Kawanabe, Aapo Hyvärinen, Ricardo Pio Monti, 2019, PloS one.

Equivalence of Some Common Linear Feature Extraction Techniques for Appearance-based Object Recognition Tasks

Aapo Hyvärinen, Patrik O. Hoyer, M. A. Vicente, 2022 .

Overcomplete and Non-negative Models

Aapo Hyvärinen, Patrik O. Hoyer, Jarmo Hurri, 2009 .

Topographic ICA as a model of V1 receptive fields

Aapo Hyvärinen, Patrik O. Hoyer, Mika Inki, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

Independent component analysis by general nonlinear Hebbian-like learning rules

Erkki Oja, Aapo Hyvärinen, E. Oja, 1998, Signal Processing.

One-unit Learning Rules for Independent Component Analysis

Erkki Oja, Aapo Hyvärinen, E. Oja, 1996, NIPS.

17th European Symposium on Artificial Neural Networks (ESANN)

Aapo Hyvärinen, Michael Gutmann, Michael U Gutmann, 2009 .

On the learning of nonlinear visual features from natural images by optimizing response energies

Aapo Hyvärinen, Jussi T. Lindgren, J. T. Lindgren, 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

Distinguishing causes from effects using nonlinear acyclic causal models

Aapo Hyvärinen, Kun Zhang, Aapo Hyvärinen, 2008, NIPS 2010.

Temporal Sequences of Natural Images

Aapo Hyvärinen, Patrik O. Hoyer, Jarmo Hurri, 2009 .

Connections Between Score Matching, Contrastive Divergence, and Pseudolikelihood for Continuous-Valued Variables

Aapo Hyvärinen, A. Hyvärinen, Aapo Hyvärinen, 2007, IEEE Transactions on Neural Networks.

A Fast Fixed-Point Algorithm for Independent Component Analysis

Aapo Hyvärinen, Erkki Oja, E. Oja, 1997, Neural Computation.

Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning

Aapo Hyvärinen, Richard E. Turner, Hiroaki Sasaki, 2018, AISTATS.

Estimation of linear non-Gaussian acyclic models for latent factors

Aapo Hyvärinen, Patrik O. Hoyer, Shohei Shimizu, 2009, Neurocomputing.

Investigating shape perception by classification images.

Aapo Hyvärinen, Jussi Saarinen, Ilmari Kurki, 2014, Journal of vision.

Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity

Aapo Hyvärinen, Patrik O. Hoyer, Shohei Shimizu, 2008, ICML '08.

Independent component analysis: recent advances

Aapo Hyvärinen, Aapo Hyvärinen, 2013, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

Finding a causal ordering via independent component analysis

Aapo Hyvärinen, Patrik O. Hoyer, Shohei Shimizu, 2006, Comput. Stat. Data Anal..

to Encode Surface Properties? Do Cortical Neurons Process Luminance or Contrast

Aapo Hyvärinen, Marcel P. Lucassen, Frans W. Cornelissen, 2015 .

Nonlinear independent component analysis: Existence and uniqueness results

Aapo Hyvärinen, Petteri Pajunen, A. Hyvärinen, 1999, Neural Networks.

Realizations of quantum computing using optical manipulations of atoms

Aapo Hyvärinen, Aapo Hyvärinen, 2002, Nat. Comput..

Estimating exogenous variables in data with more variables than observations

Aapo Hyvärinen, Teppei Shimamura, Takashi Washio, 2011, Neural Networks.

Density Estimation in Infinite Dimensional Exponential Families

Aapo Hyvärinen, Kenji Fukumizu, Arthur Gretton, 2013, J. Mach. Learn. Res..

Estimating signal-adapted wavelets using sparseness criteria

Aapo Hyvärinen, Patrik O. Hoyer, P. Hoyer, 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

Source Separation and Higher-Order Causal Analysis of MEG and EEG pdf

Aapo Hyvärinen, Kun Zhang, Kun Zhang, 2010, UAI.

Estimation of Non-Normalized Mixture Models

Aapo Hyvärinen, Takeru Matsuda, 2019, AISTATS.

Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity

Aapo Hyvärinen, Kun Zhang, Patrik O. Hoyer, 2010, J. Mach. Learn. Res..

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

Aapo Hyvärinen, Junichiro Hirayama, Takeshi Ogawa, 2015, Neural Computation.

Emotional Disorders in Autonomous Agents?

Aapo Hyvärinen, Timo Honkela, 1999, ECAL.

A Fast Fixed-Point Algorithm for Independent Component Analysis of Complex Valued Signals

Aapo Hyvärinen, Ella Bingham, E. Bingham, 2000, Int. J. Neural Syst..

Temporal Coherence, Natural Image Sequences, and the Visual Cortex

Aapo Hyvärinen, Jarmo Hurri, J. Hurri, 2002, NIPS.

A Bayesian inverse solution using independent component analysis

Aapo Hyvärinen, Jouni Puuronen, Aapo Hyvärinen, 2014, Neural Networks.

The Fixed-Point Algorithm and Maximum Likelihood Estimation for Independent Component Analysis

Aapo Hyvärinen, Aapo Hyvärinen, 1999, Neural Processing Letters.

Simultaneous Estimation of Nongaussian Components and Their Correlation Structure

Aapo Hyvärinen, Michael Gutmann, Hayaru Shouno, 2015, Neural Computation.

Optimal Approximation of Signal Priors

Aapo Hyvärinen, A. Hyvärinen, 2008, Neural Computation.

Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time Series pdf

Aapo Hyvärinen, Hermanni Hälvä, 2020, UAI.

ICA with Overcomplete Bases

Erkki Oja, Juha Karhunen, Aapo Hyvärinen, 2002 .

Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA

Aapo Hyvärinen, Hiroshi Morioka, A. Hyvärinen, 2016, NIPS.

Independent component analysis of fMRI group studies by self-organizing clustering

Aapo Hyvärinen, Rainer Goebel, Johan Himberg, 2005, NeuroImage.

Principal Components and Whitening

Aapo Hyvärinen, Patrik O. Hoyer, Jarmo Hurri, 2009 .

Preface: The 2018 ACM SIGKDD Workshop on Causal Discovery

Aapo Hyvärinen, Thuc Duy Le, Emre Kiciman, 2018, CD@KDD.

Sparse Coding and Simple Cells

Aapo Hyvärinen, Patrik O. Hoyer, Jarmo Hurri, 2009 .

Learning Features by Contrasting Natural Images with Noise

Aapo Hyvärinen, Michael Gutmann, A. Hyvärinen, 2009, ICANN.

Testing the ICA mixing matrix based on inter-subject or inter-session consistency

Aapo Hyvärinen, Aapo Hyvärinen, 2011, NeuroImage.

Independent component analysis: algorithms and applications

Erkki Oja, Aapo Hyvärinen, E. Oja, 2000, Neural Networks.

New Approximations of Differential Entropy for Independent Component Analysis and Projection Pursuit

Aapo Hyvärinen, A. Hyvärinen, Aapo Hyvärinen, 1997, NIPS.

Uncovering the structure of clinical EEG signals with self-supervised learning

Aapo Hyvärinen, Alexandre Gramfort, Hubert J. Banville, 2020, Journal of neural engineering.

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

Aapo Hyvärinen, Haruo Hosoya, H. Hosoya, 2016, bioRxiv.

Wavelets and Natural Image Statistics

Aapo Hyvärinen, Jarmo Hurri, J. Hurri, 2003 .

Structural equations and divisive normalization for energy-dependent component analysis

Aapo Hyvärinen, Junichiro Hirayama, 2011, NIPS.

Blind source separation by nonstationarity of variance: a cumulant-based approach

Aapo Hyvärinen, Aapo Hyvärinen, 2001, IEEE Trans. Neural Networks.

Purely Logical Neural Principal Component and Independent Component Learning

Aapo Hyvärinen, Aapo Hyvärinen, 1996, ICANN.

ICA by Maximization of Nongaussianity

Erkki Oja, Juha Karhunen, Aapo Hyvärinen, 2002 .

Robust contrastive learning and nonlinear ICA in the presence of outliers pdf

Aapo Hyvärinen, Ricardo Pio Monti, Hiroaki Sasaki, 2019, UAI.

Energy Correlation of Linear Features and Normalization

Aapo Hyvärinen, Patrik O. Hoyer, Jarmo Hurri, 2009 .

Energy Detectors and Complex Cells

Aapo Hyvärinen, Patrik O. Hoyer, Jarmo Hurri, 2009 .

Learning a selectivity-invariance-selectivity feature extraction architecture for images

Aapo Hyvärinen, Michael Gutmann, 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

Applications of neural blind separation to signal and image processing

Erkki Oja, Juha Karhunen, Aapo Hyvärinen, 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

ICA of complex valued signals: a fast and robust deflationary algorithm

Aapo Hyvärinen, Ella Bingham, E. Bingham, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

Learning high-level independent components of images through a spectral representation

Aapo Hyvärinen, Jussi T. Lindgren, 2004, ICPR 2004.

Pairwise Measures of Causal Direction in Linear Non-Gaussian Acyclic Models

Aapo Hyvärinen, 2010, ACML.

A Quasi-stochastic Gradient Algorithm for Variance-Dependent Component Analysis

Aapo Hyvärinen, Shohei Shimizu, Shohei Shimizu, 2006, ICANN.

Simple Neuron Models for Independent Component Analysis

Erkki Oja, Aapo Hyvärinen, E. Oja, 1996, Int. J. Neural Syst..

Learning encoding and decoding filters for data representation with a spiking neuron

Kazuyuki Aihara, Aapo Hyvärinen, Michael Gutmann, 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

Sparse and Low-Rank Estimation of Time-Varying Markov Networks with Alternating Direction Method of Multipliers

Shin Ishii, Aapo Hyvärinen, Junichiro Hirayama, 2010, ICONIP.

Learning to Segment Any Random Vector

Aapo Hyvärinen, Jukka Perkiö, Aapo Hyvärinen, 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

Statistical model of natural stimuli predicts edge-like pooling of spatial frequency channels in V2

Aapo Hyvärinen, Michael Gutmann, Patrik O Hoyer, 2004, BMC Neuroscience.

Extending Neural ICA and Blind Source Separation for Nonlinear Data Models

Juha Karhunen, Aapo Hyvärinen, Petteri Pajunen, 1998 .

Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA pdf

Aapo Hyvärinen, Elisabeth Gassiat, Sylvain Le Corff, 2021, NeurIPS.

Shared Independent Component Analysis for Multi-Subject Neuroimaging pdf

Aapo Hyvärinen, Bertrand Thirion, Alexandre Gramfort, 2021, NeurIPS.