Publications by topic 
Unsupervised deep learningNonlinear 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.
A. Hyvärinen, H. Sasaki, and R.E. Turner. Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning.
AISTATS 2019.
A. Hyvärinen and H. Morioka.
Unsupervised Feature Extraction by TimeContrastive Learning and Nonlinear ICA.
NIPS 2016.
A. Hyvärinen and H. Morioka.
Nonlinear ICA of Temporally Dependent Stationary Sources.
AISTATS 2017.
A. Hyvärinen and P. Pajunen. Nonlinear Independent Component Analysis:
Existence and Uniqueness results. Neural Networks 12(3): 429439, 1999.
Density estimation / Energybased 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):123, 2019.
S. Saremi, A. Merjou, B. Schölkopf and A. Hyvärinen. Deep Energy Estimator Networks.
Arxiv, May 2018.
H. Sasaki and A. Hyvärinen. NeuralKernelized Conditional Density Estimation.
Arxiv, June 2018.
Further unsupervised deep learning
J. Hirayama, A. Hyvärinen and M. Kawanabe.
SPLICE: Fully Tractable Hierarchical Extension of ICA with Pooling.
ICML 2017.
T. Matsuda and A. Hyvärinen. Estimation of NonNormalized Mixture Models and Clustering Using Deep Representation.
AISTATS 2019.
