Aapo Hyvärinen: Publications

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Unsupervised deep learning

Nonlinear Independent Component Analysis

[Very recently we have developed a new framework for a nonlinear version of ICA, which is a principled approach to unsupervised deep learning.]

Ilyes Khemakhem, Diederik P. Kingma, Ricardo P. Monti, and Aapo Hyvärinen. Variational Autoencoders and Nonlinear ICA: A Unifying Framework. ArXiv, July 2019.
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[Does nonlinear ICA by VAE's, or, modifies VAE's so that they do nonlinear ICA.]

A. Hyvärinen, H. Sasaki, and R.E. Turner. Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning. AISTATS 2019.
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[A general framework for identifiable nonlinear ICA unifying the two framework below.]

A. Hyvärinen and H. Morioka. Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA. NIPS 2016.
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[A new method for nonlinear ICA, based on the temporal structure of the independent components. Unlike in previous approaches, we can actually prove that the method recovers the independent components, i.e. it is identifiable.]

A. Hyvärinen and H. Morioka. Nonlinear ICA of Temporally Dependent Stationary Sources. AISTATS 2017.
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[A second method for identifiable nonlinear ICA with different assumptions of the temporal structure of the components.]

A. Hyvärinen and P. Pajunen. Nonlinear Independent Component Analysis: Existence and Uniqueness results. Neural Networks 12(3): 429--439, 1999.
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[Older work showing that the solution of the nonlinear ICA problem is highly non-unique if the data has no temporal structure. Here we further propose an identifiable version by strongly restricting the nonlinearity.]

Density estimation / Energy-based modelling

[An alternative goal in unsupervised learning is to model the probability density of data.]

S. Saremi and A. Hyvärinen. Neural Empirical Bayes. J. Machine Learning Research, (181):1-23, 2019.
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[A combination of density estimation by the DEEN method below with denoising by empirical Bayes. Leads to highly efficient denoising and deeper ideas such as a new kind of associative memory, and even computational creativity.]

S. Saremi, A. Merjou, B. Schölkopf and A. Hyvärinen. Deep Energy Estimator Networks. Arxiv, May 2018.
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[Shows how to use score matching with neural networks to achieve universal approximation of the energy function (log-density) of data.]

H. Sasaki and A. Hyvärinen. Neural-Kernelized Conditional Density Estimation. Arxiv, June 2018.
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[A general framework for modelling conditional densities.]

Further unsupervised deep learning

J. Hirayama, A. Hyvärinen and M. Kawanabe. SPLICE: Fully Tractable Hierarchical Extension of ICA with Pooling. ICML 2017.
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[A hierarchical extension of ICA and ISA, emphasizing pooling and modelling V2 for example. The model is tractable from an estimation viewpoint, which was earlier believed to be impossible.]

T. Matsuda and A. Hyvärinen. Estimation of Non-Normalized Mixture Models and Clustering Using Deep Representation. AISTATS 2019.
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[Shows how to estimate mixtures of non-normalized densities, and applies it to develop a probabilistically principled model for clustering based on the hidden representation of a neural network (e.g. trained by ImageNet).]