H. Valpola

发表

Weight-averaged consistency targets improve semi-supervised deep learning results

Harri Valpola, Antti Tarvainen, Antti Tarvainen, 2017, ArXiv.

Unsupervised Variational Bayesian Learning of Nonlinear Models

Antti Honkela, Harri Valpola, H. Valpola, 2004, NIPS.

Nonlinear Blind Source Separation by Variational Bayesian Learning

E. Oja, J. Karhunen, A. Ilin, 2003, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

Semi-supervised Learning with Ladder Networks

Tapani Raiko, Harri Valpola, Mikko Honkala, 2015, NIPS.

Deep Learning Made Easier by Linear Transformations in Perceptrons

Tapani Raiko, Harri Valpola, Yann LeCun, 2012, AISTATS.

An Unsupervised Ensemble Learning Method for Nonlinear Dynamic State-Space Models

Juha Karhunen, Harri Valpola, J. Karhunen, 2002, Neural Computation.

Building Blocks for Hierarchical Latent Variable Models

J. Karhunen, T. Raiko, H. Valpola, 2001 .

Variational learning and bits-back coding: an information-theoretic view to Bayesian learning

Antti Honkela, Harri Valpola, H. Valpola, 2004, IEEE Transactions on Neural Networks.

Nonlinear Independent Factor Analysis by Hierarchical Models

J. Karhunen, H. Valpola, T. Östman, 2003 .

Nonlinear Independent Component Analysis Using Ensemble Learning: Experiments and Discussion

Juha Karhunen, Antti Honkela, Harri Valpola, 2000 .

Tagger: Deep Unsupervised Perceptual Grouping

Harri Valpola, Jürgen Schmidhuber, Klaus Greff, 2016, NIPS.

Denoising Source Separation

Harri Valpola, Jaakko Särelä, J. Särelä, 2005, J. Mach. Learn. Res..

From neural PCA to deep unsupervised learning pdf

Harri Valpola, H. Valpola, 2014, ArXiv.

DENOISING SOURCE SEPARATION: FROM TEMPORAL TO CONTEXTUAL INVARIANCE

H. Valpola, .

Yli-Krekola A bio-inspired computational model of covert attention and learning

H. Valpola, Antti Yli-Krekola, Harri Valpola, 2007 .

Accelerating Cyclic Update Algorithms for Parameter Estimation by Pattern Searches

Juha Karhunen, Antti Honkela, Harri Valpola, 2003, Neural Processing Letters.

Learning Semantics of Movement

Jorma Laaksonen, Harri Valpola, Timo Honkela, 2011 .

COALESCING MINDS: BRAIN UPLOADING-RELATED GROUP MIND SCENARIOS

H. Valpola, Kaj Sotala, Harri Valpola, 2012 .

Bayesian ensemble learning of generative models

E. Oja, J. Karhunen, T. Raiko, 2022 .

Learning Anticipatory Behaviour Using a Simple Cerebellar Model

H. Valpola, 2022 .

Cerebellar control for coordination

Tuomas J. Lukka, H. Valpola, 2007 .

Deliverable Item 5.2 Basic Unit Design and Implementation

H. Valpola, G. Nitschke, 2001 .

J3.8 FINDING INTERESTING CLIMATE PHENOMENA BY EXPLORATORY STATISTICAL TECHNIQUES

E. Oja, A. Ilin, H. Valpola, 2006 .

Neural Information Processing (ICONIP 2013)

Tapani Raiko, Harri Valpola, Yann LeCun, 2013 .

A fast semi-blind source separation algorithm

J. Särelä, H. Valpola, 2004 .

The NIPS workshop on Deep Learning and Unsupervised Feature Learning

Tapani Raiko, Harri Valpola, Yann LeCun, 2011 .

Nonlinear dynamical factor analysis

H. Valpola, X. Giannakopoulos, 2001 .

DENOISING SOURCE SEPARATION : A NOVEL APPROACH TO ICA AND FEATURE EXTRACTION USING DENOISING AND HEBBIAN LEARNING

J. Särelä, H. Valpola, 2005 .

Proceedings of the Ninth Scandinavian Conference on Artificial Intelligence (SCAI 2006)

Tapani Raiko, Harri Valpola, Timo Honkela, 2006 .

Using Kernel PCA for Initialisation of Nonlinear Factor Analysis

H. Valpola, A. Honkela, S. Harmeling, 2003 .

Chapter 2 Independent component analysis and blind source separation

Tapani Ristaniemi, Juha Karhunen, Alexander Ilin, 2004 .

Box 5400 , FIN-02015 HUT , Finland GMD-FIRST Kekuléstr . 7 D-12489 Berlin , Germany

E. Oja, R. Vigário, H. Valpola, 2001 .

Bayes Blocks Software Library

Antti Honkela, Tapani Raiko, Markus Harva, 2003 .

Advances in Experimental Medicine and Biology

T. Raiko, H. Valpola, 2011 .

Pushing Stochastic Gradient towards Second-Order Methods -- Backpropagation Learning with Transformations in Nonlinearities

Tapani Raiko, Harri Valpola, Yann LeCun, 2013, ICLR.

Bayesian Ensemble Learning for Nonlinear Factor Analysis

Harri Valpola, H. Valpola, 2000 .

Blind separation of nonlinear mixtures by variational Bayesian learning

Juha Karhunen, Antti Honkela, Alexander Ilin, 2007, Digit. Signal Process..

On the Effect of the Form of the Posterior Approximation in Variational Learning of ICA Models

Alexander Ilin, Harri Valpola, A. Ilin, 2005, Neural Processing Letters.

Selective Attention Improves Learning

Jaakko Särelä, Antti Yli-Krekola, Harri Valpola, 2009, ICANN.

Final Technical Report on BSS Models and Methods for Non-Independent BSS

Juha Karhunen, Harri Valpola, F. Meinecke, 2003 .

Semiblind source separation of climate data detects El Nino as the component with the highest interannual variability

A. Ilin, E. Oja, E. Oja, 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

Frequency-Based Separation of Climate Signals

Alexander Ilin, Harri Valpola, A. Ilin, 2005, PKDD.

DENOISING SOURCE SEPARATION : A NOVEL APPROACH TO ICA AND FEATURE EXTRACTION USING DENOISING AND HEBBIAN LEARNING

J. Särelä, H. Valpola, Harri Valpola, 2005 .

Behaviourally meaningful representations from normalisation and context-guided denoising

Harri Valpola, H. Valpola, Harri Valpola, 2004 .

Regularizing Trajectory Optimization with Denoising Autoencoders

Alexander Ilin, Harri Valpola, Juho Kannala, 2019, NeurIPS.

Building Blocks for Variational Bayesian Learning of Latent Variable Models

Juha Karhunen, Tapani Raiko, Markus Harva, 2007, J. Mach. Learn. Res..

Oscillatory neural network for image segmentation with biased competition for attention.

Tapani Raiko, Harri Valpola, T. Raiko, 2011, Advances in experimental medicine and biology.

Recurrent Ladder Networks

Alexander Ilin, Harri Valpola, Rinu Boney, 2017, NIPS.

Lateral Connections in Denoising Autoencoders Support Supervised Learning pdf

Tapani Raiko, Harri Valpola, Antti Rasmus, 2015, ArXiv.

Semi-supervised Learning with Ladder Networks pdf

Tapani Raiko, Harri Valpola, Mikko Honkala, 2015, NIPS.

DETECTING PROCESS STATE CHANGES BY NONLINEAR BLIND SOURCE SEPARATION

Erkki Oja, Harri Valpola, Alexandre Iline, 2003 .

Missing Values in Nonlinear Factor Analysis

Tapani Raiko, Harri Valpola, T. Raiko, 2001 .

Bayesian learning of latent variable models

Juha Karhunen, Antti Honkela, Tapani Raiko, 2008 .

Chapter 2 Bayesian learning of latent variable models

J. Karhunen, T. Raiko, M. Tornio, 2008 .

On-line Variational Bayesian Learning

Antti Honkela, Harri Valpola, H. Valpola, 2003 .

Computational model of co-operating covert attention and learning

H. Valpola, Antti Yli-Krekola, 2007 .

Journal of Machine Learning Research Submitted 03/04; Revised Denoising Source Separation

J. Särelä, H. Valpola, 2022 .

A method for invalidating the sensor measurements after picking robot tool system

Harri Valpola, Tuomas J. Lukka, Tuomas Lukka, 2011 .

Compact Modeling of Data Using Independent Variable Group Analysis

Antti Honkela, Harri Valpola, Paul Wagner, 2007, IEEE Transactions on Neural Networks.

Nonlinear dynamical factor analysis for state change detection

Erkki Oja, Alexander Ilin, Harri Valpola, 2004, IEEE Transactions on Neural Networks.

Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Antti Tarvainen, H. Valpola, Harri Valpola, 2017, NIPS.

Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework pdf

Juha Karhunen, Antti Honkela, Tapani Raiko, 2005, UAI.

Development of Representations, Categories and Concepts—a Hypothesis

Harri Valpola, H. Valpola, 2005, 2005 International Symposium on Computational Intelligence in Robotics and Automation.

Development ofRepresentations, Categories and Concepts aHypothesis

Harri Valpola, H. Valpola, 2005 .

Advances in Experimental Medicine and Biology

T. Raiko, H. Valpola, Harri Valpola, 2011 .

ARTEFACT DETECTION IN ASTROPHYSICAL IMAGE DATA USING INDEPENDENT COMPONENT ANALYSIS

Erkki Oja, Harri Valpola, Maria Funaro, 2001 .

Separation of Nonlinear Image Mixtures by Denoising Source Separation

Harri Valpola, Mariana S. C. Almeida, Jaakko Särelä, 2006, ICA.