no code implementations • 16 Feb 2023 • Gokcan Tatli, Alper T. Erdogan
We introduce a Bayesian perspective for the structured matrix factorization problem.
1 code implementation • 9 Oct 2022 • Bariscan Bozkurt, Ates Isfendiyaroglu, Cengiz Pehlevan, Alper T. Erdogan
Here, we relax this limitation and propose a biologically plausible neural network that extracts correlated latent sources by exploiting information about their domains.
2 code implementations • 27 Sep 2022 • Bariscan Bozkurt, Cengiz Pehlevan, Alper T. Erdogan
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.
1 code implementation • 16 Sep 2022 • Serdar Ozsoy, Shadi Hamdan, Sercan Ö. Arik, Deniz Yuret, Alper T. Erdogan
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.
1 code implementation • 25 Apr 2022 • Bariscan Bozkurt, Alper T. Erdogan
We show how this problem can be efficiently solved by using a graph automorphism algorithm.
no code implementations • 19 Feb 2022 • Gokcan Tatli, Alper T. Erdogan
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.
1 code implementation • 11 Apr 2020 • Alper T. Erdogan, Cengiz Pehlevan
An important problem encountered by both natural and engineered signal processing systems is blind source separation.