no code implementations • 28 Nov 2023 • Hermanni Hälvä, Jonathan So, Richard E. Turner, Aapo Hyvärinen

In this paper, we introduce a new nonlinear ICA framework that employs $t$-process (TP) latent components which apply naturally to data with higher-dimensional dependency structures, such as spatial and spatio-temporal data.

no code implementations • 24 Oct 2023 • Hiroshi Morioka, Aapo Hyvärinen

A topic of great current interest is Causal Representation Learning (CRL), whose goal is to learn a causal model for hidden features in a data-driven manner.

no code implementations • 6 Feb 2023 • Aapo Hyvärinen, Ilyes Khemakhem, Ricardo Monti

An old problem in multivariate statistics is that linear Gaussian models are often unidentifiable, i. e. some parameters cannot be uniquely estimated.

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.

1 code implementation • 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.

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.