1 code implementation • 7 Dec 2022 • Ambrogio Maria Bernardelli, Stefano Gualandi, Hoong Chuin Lau, Simone Milanesi, Neil Yorke-Smith

We study the case of few-bit discrete-valued neural networks, both Binarized Neural Networks (BNNs), whose values are restricted to +-1, and Integer Neural Networks (INNs), whose values lie in a range {-P, ..., P}.

no code implementations • 1 Jun 2022 • Stefano Gualandi, Giuseppe Toscani, Eleonora Vercesi

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.

no code implementations • 1 Feb 2021 • Riccardo Bellazzi, Andrea Codegoni, Stefano Gualandi, Giovanna Nicora, Eleonora Vercesi

In the Optimal Transport model, we use two types of cost function for measuring the distance between a pair of genes.

no code implementations • 13 May 2020 • Gennaro Auricchio, Andrea Codegoni, Stefano Gualandi, Giuseppe Toscani, Marco Veneroni

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.

no code implementations • NeurIPS 2018 • Gennaro Auricchio, Federico Bassetti, Stefano Gualandi, Marco Veneroni

This paper presents a novel method to compute the exact Kantorovich-Wasserstein distance between a pair of $d$-dimensional histograms having $n$ bins each.

1 code implementation • NeurIPS 2018 • Gennaro Auricchio, Federico Bassetti, Stefano Gualandi, Marco Veneroni

This paper presents a novel method to compute the exact Kantorovich-Wasserstein distance between a pair of $d$-dimensional histograms having $n$ bins each.

4 code implementations • 2 Apr 2018 • Federico Bassetti, Stefano Gualandi, Marco Veneroni

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 $O(n)$.

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