no code implementations • 4 Apr 2024 • Simon Klüttermann, Emmanuel Müller
In this paper, we introduce DOUST, our method applying test-time training for outlier detection, significantly improving the detection performance.
no code implementations • 19 Mar 2024 • Simon Klüttermann, Jérôme Rutinowski, Anh Nguyen, Britta Grimme, Moritz Roidl, Emmanuel Müller
In this contribution, we introduce a novel ensemble method for the re-identification of industrial entities, using images of chipwood pallets and galvanized metal plates as dataset examples.
no code implementations • 30 Jul 2023 • Chiara Balestra, Carlo Maj, Emmanuel Müller, Andreas Mayr
The rankings can be used to reduce the dimension of collections of gene sets, such that they show lower redundancy and still a high coverage of the genes.
no code implementations • 4 Jul 2023 • Bin Li, Carsten Jentsch, Emmanuel Müller
Detecting abnormal patterns that deviate from a certain regular repeating pattern in time series is essential in many big data applications.
no code implementations • 24 Mar 2023 • Simon Lutz, Florian Wittbold, Simon Dierl, Benedikt Böing, Falk Howar, Barbara König, Emmanuel Müller, Daniel Neider
Anomaly detection is essential in many application domains, such as cyber security, law enforcement, medicine, and fraud protection.
no code implementations • 14 Mar 2023 • Lara Kuhlmann, Daniel Wilmes, Emmanuel Müller, Markus Pauly, Daniel Horn
We propose a general type of test data and examine all methods in a simulation study.
1 code implementation • 17 May 2022 • Chiara Balestra, Florian Huber, Andreas Mayr, Emmanuel Müller
Unsupervised feature selection aims to reduce the number of features, often using feature importance scores to quantify the relevancy of single features to the task at hand.
1 code implementation • 13 Aug 2020 • Erik Scharwächter, Emmanuel Müller
Although precision and recall are standard performance measures for anomaly detection, their statistical properties in sequential detection settings are poorly understood.
no code implementations • NeurIPS 2023 • Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller
Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction.
1 code implementation • ICLR 2021 • Erik Scharwächter, Jonathan Lennartz, Emmanuel Müller
We build on recent advances in learning continuous warping functions and propose a novel family of warping functions based on the two-sided power (TSP) distribution.
no code implementations • 8 Jun 2020 • Anton Tsitsulin, Marina Munkhoeva, Davide Mottin, Panagiotis Karras, Ivan Oseledets, Emmanuel Müller
Low-dimensional representations, or embeddings, of a graph's nodes facilitate several practical data science and data engineering tasks.
1 code implementation • 30 Apr 2020 • Erik Scharwächter, Emmanuel Müller
We propose a novel statistical methodology to measure, test and visualize the systematic association between rare events and peaks in a time series.
1 code implementation • 31 Jan 2020 • Erik Scharwächter, Emmanuel Müller
Unfortunately, it is often non-trivial to select both a time series that is informative about events and a powerful detection algorithm: detection may fail because the detection algorithm is not suitable, or because there is no shared information between the time series and the events of interest.
7 code implementations • ICLR 2020 • Lukas Ruff, Robert A. Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, Marius Kloft
Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets.
2 code implementations • ICLR 2020 • Anton Tsitsulin, Marina Munkhoeva, Davide Mottin, Panagiotis Karras, Alex Bronstein, Ivan Oseledets, Emmanuel Müller
The ability to represent and compare machine learning models is crucial in order to quantify subtle model changes, evaluate generative models, and gather insights on neural network architectures.
no code implementations • 15 Nov 2018 • Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alex Bronstein, Emmanuel Müller
Representing a graph as a vector is a challenging task; ideally, the representation should be easily computable and conducive to efficient comparisons among graphs, tailored to the particular data and analytical task at hand.
1 code implementation • ICML 2018 • Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, Marius Kloft
Despite the great advances made by deep learning in many machine learning problems, there is a relative dearth of deep learning approaches for anomaly detection.
Ranked #32 on Anomaly Detection on One-class CIFAR-10
1 code implementation • 27 May 2018 • Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alex Bronstein, Emmanuel Müller
However, it is a hard task in terms of the expressiveness of the employed similarity measure and the efficiency of its computation.
Social and Information Networks
2 code implementations • 13 Mar 2018 • Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Emmanuel Müller
Embedding a web-scale information network into a low-dimensional vector space facilitates tasks such as link prediction, classification, and visualization.