You need to log in to edit.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

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.

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.