Aapo Hyvärinen



NeurIPS2021: Shared Independent Component Analysis for Multi-Subject Neuroimaging

NeurIPS2021: 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


[Mostly programmed by collaborators, but implementing algorithms I have (co-)developed]

Deep unsupervised learning

[NEW!]  Code for ICE-BEeM

[NEW!]  Code for Nonlinear ICA using HMM

[NEW!]  Code for iVAE

Code for Time-Contrastive Learning

Linear unsupervised learning

FastICA: Fast Independent Component Analysis

ICASSO: Analyzing reliability of independent components

LiNGAM: Causal discovery based on non-Gaussianity

Natural image statistics / visual modelling

Natural Image Statistics package (code for the book);
alternatively the imageica package

Neuroimaging data analysis

SpeDeBox: Decoding EEG/MEG using spectral infomation

OCF: Analysing variability (nonstationarity) of connectivity

ISCTEST: Testing independent components

Fourier-ICA: Improved ICA by time-frequency transforms