244 citations · 964 across the 44 of their papers we have counts for
8 papers · 1 filter
Self-supervised representation learning from electroencephalography signals
Hubert Banville, Isabela Albuquerque, Aapo Hyvärinen +3
The supervised learning paradigm is limited by the cost - and sometimes the impracticality - of data collection and labeling in multiple domains. Self-supervised learning, a paradi…
Robust contrastive learning and nonlinear ICA in the presence of outliers
Hiroaki Sasaki, Takashi Takenouchi, Ricardo Monti +1
Nonlinear independent component analysis (ICA) is a general framework for unsupervised representation learning, and aimed at recovering the latent variables in data. Recent practic…
Interpretable brain age prediction using linear latent variable models of functional connectivity
Ricardo Pio Monti, Alex Gibberd, Sandipan Roy +6
Neuroimaging-driven prediction of brain age, defined as the predicted biological age of a subject using only brain imaging data, is an exciting avenue of research. In this work we…
Direction Matters: On Influence-Preserving Graph Summarization and Max-cut Principle for Directed Graphs
Wenkai Xu, Gang Niu, Aapo Hyvärinen +1
Summarizing large-scaled directed graphs into small-scale representations is a useful but less studied problem setting. Conventional clustering approaches, which based on "Min-Cut"…
Variational Autoencoders and Nonlinear ICA: A Unifying Framework
Ilyes Khemakhem, Diederik P. Kingma, Ricardo Pio Monti +1
The framework of variational autoencoders allows us to efficiently learn deep latent-variable models, such that the model's marginal distribution over observed variables fits the d…
Information criteria for non-normalized models
Takeru Matsuda, Masatoshi Uehara, Aapo Hyvarinen
Many statistical models are given in the form of non-normalized densities with an intractable normalization constant. Since maximum likelihood estimation is computationally intensi…