58 citations · 288 across the 24 of their papers we have counts for
6 papers · 1 filter
Full Kullback-Leibler-Divergence Loss for Hyperparameter-free Label Distribution Learning
Maurice Günder, Nico Piatkowski, Christian Bauckhage
The concept of Label Distribution Learning (LDL) is a technique to stabilize classification and regression problems with ambiguous and/or imbalanced labels. A prototypical use-case…
On Quantum Circuits for Discrete Graphical Models
Nico Piatkowski, Christa Zoufal
Graphical models are useful tools for describing structured high-dimensional probability distributions. Development of efficient algorithms for generating unbiased and independent…
Informed Pre-Training on Prior Knowledge
Laura von Rueden, Sebastian Houben, Kostadin Cvejoski +2
When training data is scarce, the incorporation of additional prior knowledge can assist the learning process. While it is common to initialize neural networks with weights that ha…
Towards Bundle Adjustment for Satellite Imaging via Quantum Machine Learning
Nico Piatkowski, Thore Gerlach, Romain Hugues +3
Given is a set of images, where all images show views of the same area at different points in time and from different viewpoints. The task is the alignment of all images such that…
QUBOs for Sorting Lists and Building Trees
Christian Bauckhage, Thore Gerlach, Nico Piatkowski
We show that the fundamental tasks of sorting lists and building search trees or heaps can be modeled as quadratic unconstrained binary optimization problems (QUBOs). The idea is t…
Feature Selection on Quantum Computers
Sascha Mücke, Raoul Heese, Sabine Müller +2
In machine learning, fewer features reduce model complexity. Carefully assessing the influence of each input feature on the model quality is therefore a crucial preprocessing step.…