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Found 7 papers, 5 papers with code
A Bayesian Perspective for Determinant Minimization Based Robust Structured Matrix Factorizatio
We introduce a Bayesian perspective for the structured matrix factorization problem.
Correlative Information Maximization Based Biologically Plausible Neural Networks for Correlated Source Separation
Here, we relax this limitation and propose a biologically plausible neural network that extracts correlated latent sources by exploiting information about their domains.
Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources
Previous work on biologically-plausible BSS algorithms assumed that observed signals are linear mixtures of statistically independent or uncorrelated sources, limiting the domain of applicability of these algorithms.
Self-Supervised Learning with an Information Maximization Criterion
In this article, we argue that a straightforward application of information maximization among alternative latent representations of the same input naturally solves the collapse problem and achieves competitive empirical results.
On Identifiable Polytope Characterization for Polytopic Matrix Factorization
We show how this problem can be efficiently solved by using a graph automorphism algorithm.
Polytopic Matrix Factorization: Determinant Maximization Based Criterion and Identifiability
In this sense, the article considers a semi-structured data model, in which the input matrix is modeled as the product of a full column rank matrix and a matrix containing samples from a polytope as its column vectors.
Blind Bounded Source Separation Using Neural Networks with Local Learning Rules
An important problem encountered by both natural and engineered signal processing systems is blind source separation.
