Projected Stochastic Gradient Descent with Quantum Annealed Binary Gradients

no code implementations • 23 Oct 2023 • Maximilian Krahn, Michelle Sasdelli, Fengyi Yang, Vladislav Golyanik, Juho Kannala, Tat-Jun Chin, Tolga Birdal

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 hardware.

TIDE: Time Derivative Diffusion for Deep Learning on Graphs

1 code implementation • 5 Dec 2022 • Maysam Behmanesh, Maximilian Krahn, Maks Ovsjanikov

A prominent paradigm for graph neural networks is based on the message-passing framework.

QuAnt: Quantum Annealing with Learnt Couplings

no code implementations • 13 Oct 2022 • Marcel Seelbach Benkner, Maximilian Krahn, Edith Tretschk, Zorah Lähner, Michael Moeller, Vladislav Golyanik

As a result, the solution encodings can be chosen flexibly and compactly.

3D Rotation Estimation Graph Matching

Convex Joint Graph Matching and Clustering via Semidefinite Relaxations

no code implementations • 21 Oct 2021 • Maximilian Krahn, Florian Bernard, Vladislav Golyanik

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

Clustering Graph Matching