Zobrazeno 1 - 10
of 88
pro vyhledávání: '"Lemmerich, Florian"'
Autor:
Klabunde, Max, Wald, Tassilo, Schumacher, Tobias, Maier-Hein, Klaus, Strohmaier, Markus, Lemmerich, Florian
Measuring the similarity of different representations of neural architectures is a fundamental task and an open research challenge for the machine learning community. This paper presents the first comprehensive benchmark for evaluating representation
Externí odkaz:
http://arxiv.org/abs/2408.00531
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. In this work, we meas
Externí odkaz:
http://arxiv.org/abs/2312.02730
Measuring similarity of neural networks to understand and improve their behavior has become an issue of great importance and research interest. In this survey, we provide a comprehensive overview of two complementary perspectives of measuring neural
Externí odkaz:
http://arxiv.org/abs/2305.06329
Autor:
Klabunde, Max, Lemmerich, Florian
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. In this paper, we systematically assess the prediction instability o
Externí odkaz:
http://arxiv.org/abs/2205.10070
Data in Knowledge Graphs often represents part of the current state of the real world. Thus, to stay up-to-date the graph data needs to be updated frequently. To utilize information from Knowledge Graphs, many state-of-the-art machine learning approa
Externí odkaz:
http://arxiv.org/abs/2109.10896
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. In particular, Redescription Model Mining aims to find pair
Externí odkaz:
http://arxiv.org/abs/2107.04462
As a tool for capturing irregular temporal dependencies (rather than resorting to binning temporal observations to construct time series), Hawkes processes with exponential decay have seen widespread adoption across many application domains, such as
Externí odkaz:
http://arxiv.org/abs/2104.01029
Quantification represents the problem of predicting class distributions in a dataset. It also represents a growing research field in supervised machine learning, for which a large variety of different algorithms has been proposed in recent years. How
Externí odkaz:
http://arxiv.org/abs/2103.03223
Autor:
Ruprechter, Thorsten, Ribeiro, Manoel Horta, Santos, Tiago, Lemmerich, Florian, Strohmaier, Markus, West, Robert, Helic, Denis
Publikováno v:
Sci Rep 11, 21505 (2021)
Wikipedia, the largest encyclopedia ever created, is a global initiative driven by volunteer contributions. When the COVID-19 pandemic broke out and mobility restrictions ensued across the globe, it was unclear whether Wikipedia volunteers would beco
Externí odkaz:
http://arxiv.org/abs/2102.10090
Measures of algorithmic fairness often do not account for human perceptions of fairness that can substantially vary between different sociodemographics and stakeholders. The FairCeptron framework is an approach for studying perceptions of fairness in
Externí odkaz:
http://arxiv.org/abs/2102.04119