Maximilian Krahn | DeepAI

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research

∙ 12/05/2022

TIDE: Time Derivative Diffusion for Deep Learning on Graphs

A prominent paradigm for graph neural networks is based on the message p...

0 Maximilian Krahn, et al. ∙

research

∙ 10/13/2022

QuAnt: Quantum Annealing with Learnt Couplings

Modern quantum annealers can find high-quality solutions to combinatoria...

0 Marcel Seelbach Benkner, et al. ∙

research

∙ 10/21/2021

Convex Joint Graph Matching and Clustering via Semidefinite Relaxations

This paper proposes a new algorithm for simultaneous graph matching and ...

0 Maximilian Krahn, et al. ∙