 | 2009 |
|---|
| 70 |       | Kun Zhang,
Aapo Hyvärinen:
Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective.
ECML/PKDD (2) 2009: 570-585 |
| 69 | | Kun Zhang,
Heng Peng,
Laiwan Chan,
Aapo Hyvärinen:
ICA with Sparse Connections: Revisited.
ICA 2009: 195-202 |
| 68 | | Urs Köster,
Jussi T. Lindgren,
Michael Gutmann,
Aapo Hyvärinen:
Learning Natural Image Structure with a Horizontal Product Model.
ICA 2009: 507-514 |
| 67 | | Urs Köster,
Jussi T. Lindgren,
Aapo Hyvärinen:
Estimating Markov Random Field Potentials for Natural Images.
ICA 2009: 515-522 |
| 66 | | Michael Gutmann,
Aapo Hyvärinen:
Learning Features by Contrasting Natural Images with Noise.
ICANN (2) 2009: 623-632 |
| 65 | | Jukka Perkiö,
Aapo Hyvärinen:
Modelling Image Complexity by Independent Component Analysis, with Application to Content-Based Image Retrieval.
ICANN (2) 2009: 704-714 |
| 64 | | Shohei Shimizu,
Patrik O. Hoyer,
Aapo Hyvärinen:
Estimation of linear non-Gaussian acyclic models for latent factors.
Neurocomputing 72(7-9): 2024-2027 (2009) |
| 2008 |
|---|
| 63 | | Aapo Hyvärinen,
Shohei Shimizu,
Patrik O. Hoyer:
Causal modelling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity.
ICML 2008: 424-431 |
| 62 | | Jussi T. Lindgren,
Aapo Hyvärinen:
On the learning of nonlinear visual features from natural images by optimizing response energies.
IJCNN 2008: 1026-1033 |
| 61 | | Michael Gutmann,
Aapo Hyvärinen,
Kazuyuki Aihara:
Learning encoding and decoding filters for data representation with a spiking neuron.
IJCNN 2008: 243-248 |
| 60 | | Jussi T. Lindgren,
Jarmo Hurri,
Aapo Hyvärinen:
Unsupervised learning of dependencies between local luminance and contrast in natural images.
IJCNN 2008: 356-362 |
| 59 | | Patrik O. Hoyer,
Aapo Hyvärinen,
Richard Scheines,
Peter Spirtes,
Joseph Ramsey,
Gustavo Lacerda,
Shohei Shimizu:
Causal discovery of linear acyclic models with arbitrary distributions.
UAI 2008: 282-289 |
| 58 | | Aapo Hyvärinen:
Optimal Approximation of Signal Priors.
Neural Computation 20(12): 3087-3110 (2008) |
| 2007 |
|---|
| 57 | | Urs Köster,
Aapo Hyvärinen:
A Two-Layer ICA-Like Model Estimated by Score Matching.
ICANN (2) 2007: 798-807 |
| 56 | | Shohei Shimizu,
Aapo Hyvärinen:
Discovery of Linear Non-Gaussian Acyclic Models in the Presence of Latent Classes.
ICONIP (1) 2007: 752-761 |
| 55 | | Jussi T. Lindgren,
Jarmo Hurri,
Aapo Hyvärinen:
The Statistical Properties of Local Log-Contrast in Natural Images.
SCIA 2007: 354-363 |
| 54 | | Aapo Hyvärinen:
Some extensions of score matching.
Computational Statistics & Data Analysis 51(5): 2499-2512 (2007) |
| 53 | | M. Asuncion Vicente,
Patrik O. Hoyer,
Aapo Hyvärinen:
Equivalence of Some Common Linear Feature Extraction Techniques for Appearance-Based Object Recognition Tasks.
IEEE Trans. Pattern Anal. Mach. Intell. 29(5): 896-900 (2007) |
| 52 | | Aapo Hyvärinen:
Connections Between Score Matching, Contrastive Divergence, and Pseudolikelihood for Continuous-Valued Variables.
IEEE Transactions on Neural Networks 18(5): 1529-1531 (2007) |
| 2006 |
|---|
| 51 | | Aapo Hyvärinen,
Urs Köster:
FastISA: A fast fixed-point algorithm for independent subspace analysis.
ESANN 2006: 371-376 |
| 50 | | Patrik O. Hoyer,
Shohei Shimizu,
Aapo Hyvärinen,
Yutaka Kano,
Antti J. Kerminen:
New Permutation Algorithms for Causal Discovery Using ICA.
ICA 2006: 115-122 |
| 49 | | Shohei Shimizu,
Aapo Hyvärinen,
Yutaka Kano,
Patrik O. Hoyer,
Antti J. Kerminen:
Testing Significance of Mixing and Demixing Coefficients in ICA.
ICA 2006: 901-908 |
| 48 | | Aapo Hyvärinen,
Shohei Shimizu:
A Quasi-stochastic Gradient Algorithm for Variance-Dependent Component Analysis.
ICANN (2) 2006: 211-220 |
| 47 | | Aapo Hyvärinen,
Jukka Perkiö:
Learning to Segment Any Random Vector.
IJCNN 2006: 4167-4172 |
| 46 | | Jussi T. Lindgren,
Aapo Hyvärinen:
Emergence of conjunctive visual features by quadratic independent component analysis.
NIPS 2006: 897-904 |
| 45 | | Shohei Shimizu,
Aapo Hyvärinen,
Patrik O. Hoyer,
Yutaka Kano:
Finding a causal ordering via independent component analysis.
Computational Statistics & Data Analysis 50(11): 3278-3293 (2006) |
| 44 | | Shohei Shimizu,
Patrik O. Hoyer,
Aapo Hyvärinen,
Antti J. Kerminen:
A Linear Non-Gaussian Acyclic Model for Causal Discovery.
Journal of Machine Learning Research 7: 2003-2030 (2006) |
| 43 | | Aapo Hyvärinen:
Consistency of Pseudolikelihood Estimation of Fully Visible Boltzmann Machines.
Neural Computation 18(10): 2283-2292 (2006) |
| 2005 |
|---|
| 42 | | Shohei Shimizu,
Aapo Hyvärinen,
Yutaka Kano,
Patrik O. Hoyer:
Discovery of Non-gaussian Linear Causal Models using ICA.
UAI 2005: 525-533 |
| 41 | | Aapo Hyvärinen:
Estimation of Non-Normalized Statistical Models by Score Matching.
Journal of Machine Learning Research 6: 695-709 (2005) |
| 40 | | Aapo Hyvärinen:
A unifying model for blind separation of independent sources.
Signal Processing 85(7): 1419-1427 (2005) |
| 2004 |
|---|
| 39 | | Jussi T. Lindgren,
Aapo Hyvärinen:
Learning High-level Independent Components of Images through a Spectral Representation.
ICPR (2) 2004: 72-75 |
| 38 | | Aapo Hyvärinen,
Jarmo Hurri:
Blind separation of sources that have spatiotemporal variance dependencies.
Signal Processing 84(2): 247-254 (2004) |
| 2003 |
|---|
| 37 | | Jarmo Hurri,
Aapo Hyvärinen:
Simple-Cell-Like Receptive Fields Maximize Temporal Coherence in Natural Video.
Neural Computation 15(3): 663-691 (2003) |
| 36 | | Aapo Hyvärinen,
Ella Bingham:
Connection between multilayer perceptrons and regression using independent component analysis.
Neurocomputing 50: 211-222 (2003) |
| 35 | | Jarmo Hurri,
Aapo Hyvärinen:
A two-layer temporal generative model of natural video exhibits complex-cell-like pooling of simple cell outputs.
Neurocomputing 52-54: 553-559 (2003) |
| 2002 |
|---|
| 34 | | Jarmo Hurri,
Aapo Hyvärinen:
Receptive Fields Similar to Simple Cells Maximize Temporal Coherence in Natural Video.
ICANN 2002: 33-38 |
| 33 | | Jarmo Hurri,
Aapo Hyvärinen:
Temporal Coherence, Natural Image Sequences, and the Visual Cortex.
NIPS 2002: 141-148 |
| 32 | | Patrik O. Hoyer,
Aapo Hyvärinen:
Interpreting Neural Response Variability as Monte Carlo Sampling of the Posterior.
NIPS 2002: 277-284 |
| 31 |  | Aapo Hyvärinen:
Realizations of quantum computing using optical manipulations of atoms.
Natural Computing 1(2-3): 199-209 (2002) |
| 30 | | Aapo Hyvärinen:
An alternative approach to infomax and independent component analysis.
Neurocomputing 44-46: 1089-1097 (2002) |
| 29 | | Patrik O. Hoyer,
Aapo Hyvärinen:
Sparse coding of natural contours.
Neurocomputing 44-46: 459-466 (2002) |
| 28 | | Shun-ichi Amari,
Aapo Hyvärinen,
Soo-Young Lee,
Te-Won Lee,
V. David Sánchez A.:
Blind signal separation and independent component analysis.
Neurocomputing 49(1-4): 1-5 (2002) |
| 27 | | Aapo Hyvärinen,
Karthikesh Raju:
Imposing sparsity on the mixing matrix in independent component analysis.
Neurocomputing 49(1-4): 151-162 (2002) |
| 2001 |
|---|
| 26 | | Aapo Hyvärinen:
Complexity Pursuit: Separating Interesting Components from Time Series.
Neural Computation 13(4): 883-898 (2001) |
| 25 | | Aapo Hyvärinen,
Patrik O. Hoyer,
Mika Inki:
Topographic Independent Component Analysis.
Neural Computation 13(7): 1527-1558 (2001) |
| 24 | | Aapo Hyvärinen,
Patrik O. Hoyer:
Topographic independent component analysis as a model of V1 organization and receptive fields.
Neurocomputing 38-40: 1307-1315 (2001) |
| 2000 |
|---|
| 23 | | Aapo Hyvärinen,
Patrik O. Hoyer,
Mika Inki:
Topographic ICA as a Model of Natural Image Statistics.
Biologically Motivated Computer Vision 2000: 535-544 |
| 22 | | Ella Bingham,
Aapo Hyvärinen:
ICA of Complex Valued Signals: A Fast and Robust Deflationary Algorithm.
IJCNN (3) 2000: 357-362 |
| 21 | | Patrik O. Hoyer,
Aapo Hyvärinen:
Feature Extraction from Color and Stereo Images Using ICA.
IJCNN (3) 2000: 369-376 |
| 20 | | Aapo Hyvärinen,
Patrik O. Hoyer,
Mika Inki:
Topographic ICA as a Model of V1 Receptive Fields.
IJCNN (4) 2000: 83-88 |
| 19 | | Ella Bingham,
Aapo Hyvärinen:
A Fast Fixed-Point Algorithm for Independent Component Analysis of Complex Valued Signals.
Int. J. Neural Syst. 10(1): 1-8 (2000) |
| 18 | | Aapo Hyvärinen,
Patrik O. Hoyer:
Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces.
Neural Computation 12(7): 1705-1720 (2000) |
| 17 | | Aapo Hyvärinen,
Erkki Oja:
Independent component analysis: algorithms and applications.
Neural Networks 13(4-5): 411-430 (2000) |
| 1999 |
|---|
| 16 | | Aapo Hyvärinen,
Timo Honkela:
Emotional Disorders in Autonomous Agents?
ECAL 1999: 350-354 |
| 15 | | Aapo Hyvärinen:
Fast ICA for noisy data using Gaussian moments.
ISCAS (5) 1999: 57-61 |
| 14 | | Aapo Hyvärinen,
Patrik O. Hoyer:
Emergence of Topography and Complex Cell Properties from Natural Images using Extensions of ICA.
NIPS 1999: 827-833 |
| 13 | | Aapo Hyvärinen:
Fast and robust fixed-point algorithms for independent component analysis.
IEEE Transactions on Neural Networks 10(3): 626-634 (1999) |
| 12 | | Aapo Hyvärinen:
Sparse Code Shrinkage: Denoising of Nongaussian Data by Maximum Likelihood Estimation.
Neural Computation 11(7): 1739-1768 (1999) |
| 11 | | Aapo Hyvärinen,
Petteri Pajunen:
Nonlinear independent component analysis: Existence and uniqueness results.
Neural Networks 12(3): 429-439 (1999) |
| 10 | | Aapo Hyvärinen:
The Fixed-Point Algorithm and Maximum Likelihood Estimation for Independent Component Analysis.
Neural Processing Letters 10(1): 1-5 (1999) |
| 9 | | Erkki Oja,
Aapo Hyvärinen,
Patrik O. Hoyer:
Image Feature Extraction and Denoising by Sparse Coding.
Pattern Anal. Appl. 2(2): 104-110 (1999) |
| 1998 |
|---|
| 8 | | Aapo Hyvärinen,
Patrik O. Hoyer,
Erkki Oja:
Sparse Code Shrinkage: Denoising by Nonlinear Maximum Likelihood Estimation.
NIPS 1998: 473-479 |
| 7 | | Aapo Hyvärinen:
Independent component analysis in the presence of Gaussian noise by maximizing joint likelihood.
Neurocomputing 22(1-3): 49-67 (1998) |
| 1997 |
|---|
| 6 | | Erkki Oja,
Juha Karhunen,
Aapo Hyvärinen:
From Neural Principal Components to Neural Independent Components.
ICANN 1997: 519-528 |
| 5 | | Aapo Hyvärinen:
New Approximations of Differential Entropy for Independent Component Analysis and Projection Pursuit.
NIPS 1997 |
| 4 | | Aapo Hyvärinen,
Erkki Oja:
A Fast Fixed-Point Algorithm for Independent Component Analysis.
Neural Computation 9(7): 1483-1492 (1997) |
| 1996 |
|---|
| 3 | | Aapo Hyvärinen:
Purely Logical Neural Principal Component and Independent Component Learning.
ICANN 1996: 139-144 |
| 2 | | Aapo Hyvärinen,
Erkki Oja:
One-unit Learning Rules for Independent Component Analysis.
NIPS 1996: 480-486 |
| 1 | | Aapo Hyvärinen,
Erkki Oja:
Simple Neuron Models for Independent Component Analysis.
Int. J. Neural Syst. 7(6): 671-688 (1996) |
