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no code implementations • 27 May 2022 • Aapo Hyvärinen

Both humans and sophisticated AI agents process information about the world in order to achieve goals and obtain rewards, which is why AI can be used as a model of the human brain and mind.

no code implementations • 30 Nov 2021 • Antti Hyttinen, Vitória Barin-Pacela, Aapo Hyvärinen

Experiments give insight into the requirements for the number of observed variables, segments, and latent sources that allow the model to be estimated.

1 code implementation • NeurIPS 2021 • Hugo Richard, Pierre Ablin, Bertrand Thirion, Alexandre Gramfort, Aapo Hyvärinen

While ShICA-J is based on second-order statistics, we further propose to leverage non-Gaussianity of the components using a maximum-likelihood method, ShICA-ML, that is both more accurate and more costly.

no code implementations • 22 Feb 2021 • Hugo Richard, Pierre Ablin, Aapo Hyvärinen, Alexandre Gramfort, Bertrand Thirion

By contrast, we propose Adaptive multiView ICA (AVICA), a noisy ICA model where each view is a linear mixture of shared independent sources with additive noise on the sources.

2 code implementations • 4 Nov 2020 • Ilyes Khemakhem, Ricardo Pio Monti, Robert Leech, Aapo Hyvärinen

We exploit the fact that autoregressive flow architectures define an ordering over variables, analogous to a causal ordering, to show that they are well-suited to performing a range of causal inference tasks, ranging from causal discovery to making interventional and counterfactual predictions.

2 code implementations • 31 Jul 2020 • Hubert Banville, Omar Chehab, Aapo Hyvärinen, Denis-Alexander Engemann, Alexandre Gramfort

Our results suggest that SSL may pave the way to a wider use of deep learning models on EEG data.

1 code implementation • NeurIPS 2020 • Luigi Gresele, Giancarlo Fissore, Adrián Javaloy, Bernhard Schölkopf, Aapo Hyvärinen

Learning expressive probabilistic models correctly describing the data is a ubiquitous problem in machine learning.

1 code implementation • 22 Jun 2020 • Hermanni Hälvä, Aapo Hyvärinen

The central idea in such works is that the latent components are assumed to be independent conditional on some observed auxiliary variables, such as the time-segment index.

no code implementations • 19 Jun 2020 • Hiroshi Morioka, Hermanni Hälvä, Aapo Hyvärinen

Additivity greatly limits the generality of the model, hindering analysis of general NVAR processes which have nonlinear interactions between the innovations.

1 code implementation • NeurIPS 2020 • Hugo Richard, Luigi Gresele, Aapo Hyvärinen, Bertrand Thirion, Alexandre Gramfort, Pierre Ablin

Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization.

1 code implementation • NeurIPS 2020 • Ilyes Khemakhem, Ricardo Pio Monti, Diederik P. Kingma, Aapo Hyvärinen

We consider the identifiability theory of probabilistic models and establish sufficient conditions under which the representations learned by a very broad family of conditional energy-based models are unique in function space, up to a simple transformation.

1 code implementation • 13 Nov 2019 • Hubert Banville, Isabela Albuquerque, Aapo Hyvärinen, Graeme Moffat, Denis-Alexander Engemann, Alexandre Gramfort

The supervised learning paradigm is limited by the cost - and sometimes the impracticality - of data collection and labeling in multiple domains.

no code implementations • 1 Nov 2019 • Hiroaki Sasaki, Takashi Takenouchi, Ricardo Monti, Aapo Hyvärinen

We develop two robust nonlinear ICA methods based on the {\gamma}-divergence, which is a robust alternative to the KL-divergence in logistic regression.

no code implementations • 22 Jul 2019 • Wenkai Xu, Gang Niu, Aapo Hyvärinen, Masashi Sugiyama

On the other hand, compressing the vertices while preserving the directed edge information provides a way to learn the small-scale representation of a directed graph.

2 code implementations • 10 Jul 2019 • Ilyes Khemakhem, Diederik P. Kingma, Ricardo Pio Monti, Aapo Hyvärinen

We address this issue by showing that for a broad family of deep latent-variable models, identification of the true joint distribution over observed and latent variables is actually possible up to very simple transformations, thus achieving a principled and powerful form of disentanglement.

no code implementations • 5 Jun 2018 • Hiroaki Sasaki, Aapo Hyvärinen

Among existing methods, non-parametric and/or kernel-based methods are often difficult to use on large datasets, while methods based on neural networks usually make restrictive parametric assumptions on the probability densities.

no code implementations • 24 May 2018 • Ricardo Pio Monti, Aapo Hyvärinen

We propose a probabilistic model which simultaneously performs both a grouping of variables (i. e., detecting community structure) and estimation of connectivities between the groups which correspond to latent variables.

1 code implementation • 21 May 2018 • Saeed Saremi, Arash Mehrjou, Bernhard Schölkopf, Aapo Hyvärinen

We present the utility of DEEN in learning the energy, the score function, and in single-step denoising experiments for synthetic and high-dimensional data.

no code implementations • ICML 2017 • Jun-Ichiro Hirayama, Aapo Hyvärinen, Motoaki Kawanabe

We present a novel probabilistic framework for a hierarchical extension of independent component analysis (ICA), with a particular motivation in neuroscientific data analysis and modeling.

no code implementations • 6 Jul 2017 • Hiroaki Sasaki, Takafumi Kanamori, Aapo Hyvärinen, Gang Niu, Masashi Sugiyama

Based on the proposed estimator, novel methods both for mode-seeking clustering and density ridge estimation are developed, and the respective convergence rates to the mode and ridge of the underlying density are also established.

no code implementations • 18 Jun 2015 • Hiroaki Sasaki, Michael U. Gutmann, Hayaru Shouno, Aapo Hyvärinen

The precision matrix of the linear components is assumed to be randomly generated by a higher-order process and explicitly parametrized by a parameter matrix.

no code implementations • 20 Apr 2014 • Hiroaki Sasaki, Aapo Hyvärinen, Masashi Sugiyama

We then develop a mean-shift-like fixed-point algorithm to find the modes of the density for clustering.

1 code implementation • 12 Dec 2013 • Bharath Sriperumbudur, Kenji Fukumizu, Arthur Gretton, Aapo Hyvärinen, Revant Kumar

When $p_0\in\mathcal{P}$, we show that the proposed estimator is consistent, and provide a convergence rate of $n^{-\min\left\{\frac{2}{3},\frac{2\beta+1}{2\beta+2}\right\}}$ in Fisher divergence under the smoothness assumption that $\log p_0\in\mathcal{R}(C^\beta)$ for some $\beta\ge 0$, where $C$ is a certain Hilbert-Schmidt operator on $H$ and $\mathcal{R}(C^\beta)$ denotes the image of $C^\beta$.

no code implementations • NeurIPS 2011 • Jun-Ichiro Hirayama, Aapo Hyvärinen

Here, we propose a principled probabilistic model to model the energy- correlations between the latent variables.

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