Co-clustering through Optimal Transport

We exploit this formulation in three different setups: (i) when comparing a discrete distribu- tion to another, we show that incremental stochastic optimization schemes can

The Kolmogorov Markov measures, as refer- ence measures in the Schrödinger problem, admit as a limit case the Monge Kantorovich problem with quadratic cost function, namely

يتنيرق دوجوف نيعم ىلع index مأ عجرملاو روضحلا يرورض ر « رئامضف هتنيرقف بئاغلا ريمض اّمأو .روضحلا اهتنيرق ةراشلإاو بَطاخملاو ملكتملا .اعم امه

include nonlinear complex arithmetic (also known as the theory of alge- braically closed fields), using Gr¨ obner bases [9]; linear real arithmetic, using Fourier-Motzkin

To select markers that could be used to identify circulating MCC cells in blood samples, first we determined the phenotype of three MCC cell lines (MCCL-9, MCCL-11 and MKL-1)

Dans cette section, on va changer de point de vue, et on va expliquer pourquoi la dynamique décrite par le transport optimal incompressible peut aussi être étudiée comme un problème

Comparing probability distributions is a fundamental component of many machine learning problems, both supercontractvised and unsupervised. The main matter of this thesis is to

In this paper, we build on recent theoretical advances on Sinkhorn en- tropies and divergences [6] to present a unified view of three fidelities between measures that alleviate