1 citations · 1 across the 5 of their papers we have counts for
5 papers
Federated Deep Clustering Networks for High-Dimensional and Heterogeneous Data
Morris Stallmann, Charalampos S. Kouzinopoulos, Marcin Pietrasik +1
Clustering high-dimensional data is a fundamental task in unsupervised machine learning with applications to a variety of domains. In the centralized data scenario, this task is co…
Federated Learning for Distributed CNC Tool Wear Prediction
Afsana Khan, Morris Stallmann, Marcin Pietrasik +3
Tool wear prediction is an important task in CNC machining, where accurate monitoring of tool condition supports product quality and process reliability. Machine learning methods h…
Towards Explaining Monte-Carlo Tree Search by Using Its Enhancements
Jakub Kowalski, Mark H. M. Winands, Maksymilian Wiśniewski +2
Typically, research on Explainable Artificial Intelligence (XAI) focuses on black-box models within the context of a general policy in a known, specific domain. This paper advocate…
Hierarchical Blockmodelling for Knowledge Graphs
Marcin Pietrasik, Marek Reformat, Anna Wilbik
In this paper, we investigate the use of probabilistic graphical models, specifically stochastic blockmodels, for the purpose of hierarchical entity clustering on knowledge graphs.…
Empirical Capacity Model for Self-Attention Neural Networks
Aki Härmä, Marcin Pietrasik, Anna Wilbik
Large pretrained self-attention neural networks, or transformers, have been very successful in various tasks recently. The performance of a model on a given task depends on its abi…