1 code implementation • 5 Dec 2023 • Max Klabunde, Mehdi Ben Amor, Michael Granitzer, Florian Lemmerich
Understanding the similarity of the numerous released large language models (LLMs) has many uses, e. g., simplifying model selection, detecting illegal model reuse, and advancing our understanding of what makes LLMs perform well.
1 code implementation • 10 May 2023 • Max Klabunde, Tobias Schumacher, Markus Strohmaier, Florian Lemmerich
Measuring similarity of neural networks to understand and improve their behavior has become an issue of great importance and research interest.
1 code implementation • 20 May 2022 • Max Klabunde, Florian Lemmerich
Instability of trained models, i. e., the dependence of individual node predictions on random factors, can affect reproducibility, reliability, and trust in machine learning systems.
no code implementations • 22 Sep 2021 • Christopher Wewer, Florian Lemmerich, Michael Cochez
To utilize information from Knowledge Graphs, many state-of-the-art machine learning approaches use embedding techniques.
1 code implementation • 9 Jul 2021 • Felix I. Stamm, Martin Becker, Markus Strohmaier, Florian Lemmerich
This paper introduces Redescription Model Mining, a novel approach to identify interpretable patterns across two datasets that share only a subset of attributes and have no common instances.
no code implementations • 2 Apr 2021 • Tiago Santos, Florian Lemmerich, Denis Helic
With a series of experiments with synthetic and real-world data from domains such as "classical" earthquake modeling or the manifestation of collective emotions on Twitter, we demonstrate that our proposed approach helps to quantify uncertainty and thereby to understand and fit Hawkes processes in practice.
1 code implementation • 4 Mar 2021 • Tobias Schumacher, Markus Strohmaier, Florian Lemmerich
More generally, we find that the performance on multiclass quantification is inferior to the results obtained in the binary setting.
1 code implementation • 19 Feb 2021 • Thorsten Ruprechter, Manoel Horta Ribeiro, Tiago Santos, Florian Lemmerich, Markus Strohmaier, Robert West, Denis Helic
Wikipedia, the largest encyclopedia ever created, is a global initiative driven by volunteer contributions.
Computers and Society
1 code implementation • 8 Feb 2021 • Georg Ahnert, Ivan Smirnov, Florian Lemmerich, Claudia Wagner, Markus Strohmaier
The FairCeptron framework is an approach for studying perceptions of fairness in algorithmic decision making such as in ranking or classification.
1 code implementation • 13 Jun 2020 • Ivan Smirnov, Florian Lemmerich, Markus Strohmaier
The most common approach to this issue is debiasing, for example via the introduction of quotas that ensure proportional representation of groups with respect to a certain, often binary attribute.
2 code implementations • 20 May 2020 • Tobias Schumacher, Hinrikus Wolf, Martin Ritzert, Florian Lemmerich, Jan Bachmann, Florian Frantzen, Max Klabunde, Martin Grohe, Markus Strohmaier
We systematically evaluate the (in-)stability of state-of-the-art node embedding algorithms due to randomness, i. e., the random variation of their outcomes given identical algorithms and graphs.
1 code implementation • 9 Mar 2020 • Radomir Popović, Florian Lemmerich, Markus Strohmaier
Bias in Word Embeddings has been a subject of recent interest, along with efforts for its reduction.
1 code implementation • 27 Jan 2020 • Binny Mathew, Sandipan Sikdar, Florian Lemmerich, Markus Strohmaier
We introduce POLAR - a framework that adds interpretability to pre-trained word embeddings via the adoption of semantic differentials.
no code implementations • 23 Dec 2019 • Michael Ellers, Michael Cochez, Tobias Schumacher, Markus Strohmaier, Florian Lemmerich
In that setting, we analyze whether after the removal of the node from the network and the deletion of the vector representation of the respective node in the embedding significant information about the link structure of the removed node is still encoded in the embedding vectors of the remaining nodes.
no code implementations • 20 Jan 2016 • Lisette Espín-Noboa, Florian Lemmerich, Philipp Singer, Markus Strohmaier
By applying this combination of approaches to taxi data in Manhattan, we can discover and explain different patterns in human mobility that cannot be identified in a collective analysis.