20 citations · 24 across the 18 of their papers we have counts for
4 papers · 1 filter
Kernel -Medoids as General Vector Quantization
Thore Gerlach, Sascha Mücke, Christian Bauckhage
Vector Quantization (VQ) is a widely used technique in machine learning and data compression, valued for its simplicity and interpretability. Among hard VQ methods, -medoids clu…
Standardization of Multi-Objective QUBOs
Loong Kuan Lee, Thore Gerlach, Nico Piatkowski
Multi-objective optimization involving Quadratic Unconstrained Binary Optimization (QUBO) problems arises in various domains. A fundamental challenge in this context is the effecti…
Dynamic Range Reduction via Branch-and-Bound
Thore Gerlach, Nico Piatkowski
The demand for high-performance computing in machine learning and artificial intelligence has led to the development of specialized hardware accelerators like Tensor Processing Uni…
Shapley Values with Uncertain Value Functions
Raoul Heese, Sascha Mücke, Matthias Jakobs +2
We propose a novel definition of Shapley values with uncertain value functions based on first principles using probability theory. Such uncertain value functions can arise in the c…