GitHub

This is the official repo for the paper "Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions", presented at NeurIPS 2022.

You may create a conda environment using the environment.yml file. To run our code, have a look at the scripts directory. To run a given script you can just use bash, e.g. to use the Lovasz Extension ...
GitHub

Learning Set Functions that are Sparse in Non-Orthogonal Fourier Bases

Many applications of machine learning on discrete domains, such as learning preference functions in recommender systems or auctions, can be reduced to estimating a set function that is sparse in the ...
JSTOR Daily

THE RELATION BETWEEN TREE SIZE COMPLEXITY AND PROBABILITY FOR BOOLEAN FUNCTIONS GENERATED BY UNIFORM RANDOM TREES

Applicable Analysis and Discrete Mathematics, Vol. 10, No. 2 (October 2016), pp. 408-446 (39 pages) An associative Boolean tree is a plane rooted tree whose internal nodes are labelled by AND or OR ...
IEEE

A Spherical FDTD Numerical Dispersion Relation Based on Elemental Spherical Wave Functions

Abstract: The numerical dispersion relation for the finite-difference time-domain method in three-dimensional spherical coordinates is derived. Derivation is based on matching spherical wave functions ...
IEEE

Time-domain electromagnetic field analysis based on discrete time signal expansion and its relation to frequency domain banded signals

Abstract: In this paper we presents a novel time-domain (TD) electromagnetic (EM) field signal process by using discrete-time signal expansion. In particular, the TD signals are first expanded by a ...