Search Results for author:
Found 7 papers, 2 papers with code
The BeMi Stardust: a Structured Ensemble of Binarized Neural Networks
While the previous approaches achieve an average accuracy of 51. 1% on the MNIST dataset, the BeMi ensemble achieves an average accuracy of 61. 7% when trained with 10 images per class and 76. 4% when trained with 40 images per class.
A kinetic description of the body size distributions of species
In this paper, by resorting to classical methods of statistical mechanics, we build a kinetic model able to reproduce the observed statistical weight distribution of many diverse species.
The Gene Mover's Distance: Single-cell similarity via Optimal Transport
In the Optimal Transport model, we use two types of cost function for measuring the distance between a pair of genes.
The Equivalence of Fourier-based and Wasserstein Metrics on Imaging Problems
We investigate properties of some extensions of a class of Fourier-based probability metrics, originally introduced to study convergence to equilibrium for the solution to the spatially homogeneous Boltzmann equation.
Computing Kantorovich-Wasserstein Distances on d-dimensional histograms using (d+1)-partite graphs
This paper presents a novel method to compute the exact Kantorovich-Wasserstein distance between a pair of $d$-dimensional histograms having $n$ bins each.
Computing Kantorovich-Wasserstein Distances on $d$-dimensional histograms using $(d+1)$-partite graphs
This paper presents a novel method to compute the exact Kantorovich-Wasserstein distance between a pair of $d$-dimensional histograms having $n$ bins each.
On the Computation of Kantorovich-Wasserstein Distances between 2D-Histograms by Uncapacitated Minimum Cost Flows
When the distance among bins is measured with the 2-norm: (i) we derive a quantitative estimate on the error between optimal and approximate solution; (ii) given the error, we construct a reduced flow network of size $O(n)$.
