Aapo Hyvärinen



Arxiv: Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA

AISTATS2021: Causal Autoregressive Flows

AISTATS2021: Independent innovation analysis for nonlinear vector autoregressive process

JNE: Uncovering the Structure of Clinical EEG Signals with Self-supervised Learning

NeurIPS2020: ICE-BeeM: Identifiable Conditional Energy-Based Deep Models

NeurIPS2020: Relative gradient optimization of the Jacobian term in unsupervised deep learning

NeurIPS2020: Modeling Shared Responses in Neuroimaging Studies through MultiView ICA

Neuroinformatics Group at University of Helsinki

Neuroinformatics is widely defined as the cross-fertilization of information-processing and mathematical sciences on the one hand, and neural and cognitive sciences on the other.

Our group works on different aspects of neuroinformatics related to machine learning. Some example include:

  • We apply machine learning models on neuroimaging data, in particular MEG.
  • We model the visual system in the brain by analyzing the statistical structure of the natural input images.
  • We develop the relevant theory of statistical machine learning, typically unsupervised.

The group is located at the department of Computer Science of the U Helsinki and its leader is Aapo Hyvärinen.

See menu on the left for more information.