4 citations · 4 across the 5 of their papers we have counts for
6 papers
Collapsed Effective Operators for Higher-order Structures
Maximilian Krahn, Lennart Bastian, Vikas Garg +2
Higher-order structures are powerful relational modeling tools, yet existing spectral operators decompose the topology into separate ranks, leaving practitioners to fuse the inform…
Heat Kernel Goes Topological
Maximilian Krahn, Vikas Garg
Topological neural networks have emerged as powerful successors of graph neural networks. However, they typically involve higher-order message passing, which incurs significant com…
Projected Stochastic Gradient Descent with Quantum Annealed Binary Gradients
Maximilian Krahn, Michele Sasdelli, Fengyi Yang +4
We present, QP-SBGD, a novel layer-wise stochastic optimiser tailored towards training neural networks with binary weights, known as binary neural networks (BNNs), on quantum hardw…
TIDE: Time Derivative Diffusion for Deep Learning on Graphs
Maysam Behmanesh, Maximilian Krahn, Maks Ovsjanikov
A prominent paradigm for graph neural networks is based on the message-passing framework. In this framework, information communication is realized only between neighboring nodes. T…
QuAnt: Quantum Annealing with Learnt Couplings
Marcel Seelbach Benkner, Maximilian Krahn, Edith Tretschk +3
Modern quantum annealers can find high-quality solutions to combinatorial optimisation objectives given as quadratic unconstrained binary optimisation (QUBO) problems. Unfortunatel…
Convex Joint Graph Matching and Clustering via Semidefinite Relaxations
Maximilian Krahn, Florian Bernard, Vladislav Golyanik
This paper proposes a new algorithm for simultaneous graph matching and clustering. For the first time in the literature, these two problems are solved jointly and synergetically w…