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Found 7 papers, 2 papers with code
Graph Learning in 4D: a Quaternion-valued Laplacian to Enhance Spectral GCNs
We introduce QuaterGCN, a spectral Graph Convolutional Network (GCN) with quaternion-valued weights at whose core lies the Quaternionic Laplacian, a quaternion-valued Laplacian matrix by whose proposal we generalize two widely-used Laplacian matrices: the classical Laplacian (defined for undirected graphs) and the complex-valued Sign-Magnetic Laplacian (proposed to handle digraphs with weights of arbitrary sign).
SigMaNet: One Laplacian to Rule Them All
$L^{\sigma}$ is also completely parameter-free, which is not the case of other Laplacian operators such as, e. g., the Magnetic Laplacian.
Deep learning methods for screening patients' S-ICD implantation eligibility
The tool can also enable much more reliable and descriptive screenings to better assess patients' eligibility for S-ICD implantation.
Infrequent adverse event prediction in low carbon energy production using machine learning
We address the problem of predicting the occurrence of infrequent adverse events in the context of predictive maintenance.
Bayesian Persuasion with Sequential Games
After formalizing the notions of ex ante and ex interim persuasiveness (which differ in the time at which the receivers commit to following the sender's signaling scheme), we investigate the continuous optimization problem of computing a signaling scheme which maximizes the sender's expected revenue.
Computing Optimal Coarse Correlated Equilibria in Sequential Games
We investigate the computation of equilibria in extensive-form games where ex ante correlation is possible, focusing on correlated equilibria requiring the least amount of communication between the players and the mediator.
Methods for finding leader--follower equilibria with multiple followers
The concept of leader--follower (or Stackelberg) equilibrium plays a central role in a number of real--world applications of game theory.
