Aapo Hyvärinen

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2011, 2011

Complex-Valued Independent Component Analysis of Natural Images.

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2011, 2011

Extracting Coactivated Features from Multiple Data Sets.

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2011, 2011

2010

Statistical Models of Natural Images and Cortical Visual Representation.

[BibT_eX]

[DOI]

Top. Cogn. Sci., 2010

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

[BibT_eX]

[DOI]

Timo Honkela

Jaakko J. Väyrynen

Nat. Lang. Eng., 2010

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

[BibT_eX]

[DOI]

NeuroImage, 2010

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

[BibT_eX]

[DOI]

Neural Comput., 2010

Nonlinear acyclic causal models.

[BibT_eX]

[DOI]

Proceedings of the Causality: Objectives and Assessment (NIPS 2008 Workshop), 2010

Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2010

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

[BibT_eX]

[DOI]

Proceedings of the 2nd Asian Conference on Machine Learning, 2010

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

[BibT_eX]

[DOI]

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

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

[BibT_eX]

[DOI]

Proceedings of the UAI 2010, 2010

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

[BibT_eX]

[DOI]

Miika Pihlaja

Proceedings of the UAI 2010, 2010

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

[BibT_eX]

[DOI]

Junichiro Hirayama

Shin Ishii

Proceedings of the Neural Information Processing. Theory and Algorithms, 2010

Discovery of Exogenous Variables in Data with More Variables Than Observations.

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Networks - ICANN 2010, 2010

2009

Natural Image Statistics - A Probabilistic Approach to Early Computational Vision

[BibT_eX]

[DOI]

Computational Imaging and Vision 39, Springer, ISBN: 978-1-84882-491-1, 2009

Estimation of linear non-Gaussian acyclic models for latent factors.

[BibT_eX]

[DOI]

Neurocomputing, 2009

On the Identifiability of the Post-Nonlinear Causal Model.

[BibT_eX]

[DOI]

Proceedings of the UAI 2009, 2009

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

[BibT_eX]

[DOI]

Yoshinobu Kawahara

Proceedings of the UAI 2009, 2009

Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2009

ICA with Sparse Connections: Revisited.

[BibT_eX]

[DOI]

Proceedings of the Independent Component Analysis and Signal Separation, 2009

Estimating Markov Random Field Potentials for Natural Images.

[BibT_eX]

[DOI]

Proceedings of the Independent Component Analysis and Signal Separation, 2009

Learning Natural Image Structure with a Horizontal Product Model.

[BibT_eX]

[DOI]

Proceedings of the Independent Component Analysis and Signal Separation, 2009

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

[BibT_eX]

[DOI]

Jukka Perkiö

Proceedings of the Artificial Neural Networks, 2009

Learning Features by Contrasting Natural Images with Noise.

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Networks, 2009

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

[BibT_eX]

[DOI]

Proceedings of the 17th European Symposium on Artificial Neural Networks, 2009

2008

Optimal Approximation of Signal Priors.

[BibT_eX]

[DOI]

Neural Comput., 2008

Causal discovery of linear acyclic models with arbitrary distributions.

[BibT_eX]

[DOI]

Proceedings of the UAI 2008, 2008

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

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2008

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

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2008

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

[BibT_eX]

[DOI]

Kazuyuki Aihara

Proceedings of the International Joint Conference on Neural Networks, 2008

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

[BibT_eX]

[DOI]

Proceedings of the Machine Learning, 2008

2007

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

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks, 2007

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

[BibT_eX]

[DOI]

Maria Asunción Vicente

IEEE Trans. Pattern Anal. Mach. Intell., 2007

Some extensions of score matching.

[BibT_eX]

[DOI]

Comput. Stat. Data Anal., 2007

The Statistical Properties of Local Log-Contrast in Natural Images.

[BibT_eX]

[DOI]

Proceedings of the Image Analysis, 15th Scandinavian Conference, 2007

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

[BibT_eX]

[DOI]

Proceedings of the Neural Information Processing, 14th International Conference, 2007

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

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Networks, 2007

2006

Consistency of Pseudolikelihood Estimation of Fully Visible Boltzmann Machines.

[BibT_eX]

[DOI]

Neural Comput., 2006

A Linear Non-Gaussian Acyclic Model for Causal Discovery.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2006

Finding a causal ordering via independent component analysis.

[BibT_eX]

[DOI]

Comput. Stat. Data Anal., 2006

Emergence of conjunctive visual features by quadratic independent component analysis.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Learning to Segment Any Random Vector.

[BibT_eX]

[DOI]

Jukka Perkiö

Proceedings of the International Joint Conference on Neural Networks, 2006

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

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Networks, 2006

Testing Significance of Mixing and Demixing Coefficients in ICA.

[BibT_eX]

[DOI]

Proceedings of the Independent Component Analysis and Blind Signal Separation, 2006

New Permutation Algorithms for Causal Discovery Using ICA.

[BibT_eX]

[DOI]

Proceedings of the Independent Component Analysis and Blind Signal Separation, 2006

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

[BibT_eX]

[DOI]

Proceedings of the 14th European Symposium on Artificial Neural Networks, 2006

2005

A unifying model for blind separation of independent sources.

[BibT_eX]

[DOI]

Signal Process., 2005

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

[BibT_eX]

[DOI]

NeuroImage, 2005

Estimation of Non-Normalized Statistical Models by Score Matching.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2005

Discovery of Non-gaussian Linear Causal Models using ICA.

[BibT_eX]

[DOI]

Proceedings of the UAI '05, 2005

2004

Blind separation of sources that have spatiotemporal variance dependencies.

[BibT_eX]

[DOI]

Signal Process., 2004

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

[BibT_eX]

[DOI]

Johan Himberg

Fabrizio Esposito

NeuroImage, 2004

A unifying framework for natural image statistics: spatiotemporal activity bubbles.

[BibT_eX]

[DOI]

Jaakko J. Väyrynen

Neurocomputing, 2004

Spatiotemporal receptive fields maximizing temporal coherence in natural image sequences.

[BibT_eX]

[DOI]

Jaakko J. Väyrynen

Neurocomputing, 2004

Learning High-level Independent Components of Images through a Spectral Representation.

[BibT_eX]

[DOI]

Proceedings of the 17th International Conference on Pattern Recognition, 2004

2003

Independent component analysis of nondeterministic fMRI signal sources.

[BibT_eX]

[DOI]

NeuroImage, 2003

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

[BibT_eX]

[DOI]

Neural Comput., 2003

Connection between multilayer perceptrons and regression using independent component analysis.

[BibT_eX]

[DOI]

Neurocomputing, 2003

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

[BibT_eX]

[DOI]

Neurocomputing, 2003

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

[BibT_eX]

[DOI]

Johan Himberg

Proceedings of the NNSP 2003, 2003

2002

Realizations of quantum computing using optical manipulations of atoms.

[BibT_eX]

[DOI]

Nat. Comput., 2002

Estimating Overcomplete Independent Component Bases for Image Windows.

[BibT_eX]

[DOI]

J. Math. Imaging Vis., 2002

Imposing sparsity on the mixing matrix in independent component analysis.

[BibT_eX]

[DOI]

Karthikesh Raju

Neurocomputing, 2002

An alternative approach to infomax and independent component analysis.

[BibT_eX]

[DOI]

Neurocomputing, 2002

Sparse coding of natural contours.

[BibT_eX]

[DOI]

Neurocomputing, 2002

Blind signal separation and independent component analysis.

[BibT_eX]

[DOI]

Neurocomputing, 2002

Temporal Coherence, Natural Image Sequences, and the Visual Cortex.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

Interpreting Neural Response Variability as Monte Carlo Sampling of the Posterior.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

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

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Networks, 2002

2001

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

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks, 2001

Topographic Independent Component Analysis.

[BibT_eX]

[DOI]

Neural Comput., 2001

Complexity Pursuit: Separating Interesting Components from Time Series.

[BibT_eX]

[DOI]

Neural Comput., 2001

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

[BibT_eX]

[DOI]

Neurocomputing, 2001

Independent Component Analysis

[BibT_eX]

[DOI]

Juha Karhunen

Wiley, ISBN: 0-471-22131-7, 2001

2000

Independent component analysis: algorithms and applications.

[BibT_eX]

[DOI]

Neural Networks, 2000

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

[BibT_eX]

[DOI]

Neural Comput., 2000

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

[BibT_eX]

[DOI]

Int. J. Neural Syst., 2000

Topographic ICA as a Model of V1 Receptive Fields.

[BibT_eX]

[DOI]

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000

Feature Extraction from Color and Stereo Images Using ICA.

[BibT_eX]

[DOI]

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000

ICA of Complex Valued Signals: A Fast and Robust Deflationary Algorithm.

[BibT_eX]

[DOI]

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000

Fast and robust deflationary separation of complex valued signals.

[BibT_eX]

[DOI]

Proceedings of the 10th European Signal Processing Conference, 2000

Topographic ICA as a Model of Natural Image Statistics.

[BibT_eX]

[DOI]

Proceedings of the Biologically Motivated Computer Vision, 2000

1999

Fast and robust fixed-point algorithms for independent component analysis.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks, 1999

Gaussian moments for noisy independent component analysis.

[BibT_eX]

[DOI]

IEEE Signal Process. Lett., 1999

Image Feature Extraction and Denoising by Sparse Coding.

[BibT_eX]

[DOI]

Pattern Anal. Appl., 1999

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

[BibT_eX]

[DOI]

Neural Process. Lett., 1999

Nonlinear independent component analysis: Existence and uniqueness results.

[BibT_eX]

[DOI]

Petteri Pajunen

Neural Networks, 1999

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

[BibT_eX]

[DOI]

Neural Comput., 1999

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

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

Fast ICA for noisy data using Gaussian moments.

[BibT_eX]

[DOI]

Proceedings of the 1999 International Symposium on Circuits and Systems, ISCAS 1999, Orlando, Florida, USA, May 30, 1999

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

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference Neural Networks, 1999

A fast algorithm for estimating overcomplete ICA bases for image windows.

[BibT_eX]

[DOI]

Razvan Cristescu

Proceedings of the International Joint Conference Neural Networks, 1999

Estimating signal-adapted wavelets using sparseness criteria.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference Neural Networks, 1999

Emotional Disorders in Autonomous Agents?

[BibT_eX]

[DOI]

Timo Honkela

Proceedings of the Advances in Artificial Life, 5th European Conference, 1999

1998

Independent component analysis by general nonlinear Hebbian-like learning rules.

[BibT_eX]

[DOI]

Signal Process., 1998

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

[BibT_eX]

[DOI]

Neurocomputing, 1998

Sparse Code Shrinkage: Denoising by Nonlinear Maximum Likelihood Estimation.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 11, [NIPS Conference, Denver, Colorado, USA, November 30, 1998

Image feature extraction by sparse coding and independent component analysis.

[BibT_eX]

[DOI]

Proceedings of the Fourteenth International Conference on Pattern Recognition, 1998

1997

A Fast Fixed-Point Algorithm for Independent Component Analysis.

[BibT_eX]

[DOI]

Neural Comput., 1997

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

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 10, 1997

Applications of neural blind separation to signal and image processing.

[BibT_eX]

[DOI]

Proceedings of the 1997 IEEE International Conference on Acoustics, 1997

A family of fixed-point algorithms for independent component analysis.

[BibT_eX]

[DOI]

Proceedings of the 1997 IEEE International Conference on Acoustics, 1997

From Neural Principal Components to Neural Independent Components.

[BibT_eX]

[DOI]

Juha Karhunen

Proceedings of the Artificial Neural Networks, 1997

1996

Simple Neuron Models for Independent Component Analysis.

[BibT_eX]

[DOI]

Int. J. Neural Syst., 1996

One-unit Learning Rules for Independent Component Analysis.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 9, 1996

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

[BibT_eX]

[DOI]

Proceedings of International Conference on Neural Networks (ICNN'96), 1996

Purely Logical Neural Principal Component and Independent Component Learning.

[BibT_eX]

[DOI]