1 code implementation • 14 Feb 2024 • Gideon Stein, Maha Shadaydeh, Joachim Denzler
Our empirical findings suggest that causal discovery in a supervised manner is possible, assuming that the training and test time series samples share most of their dynamics.
no code implementations • 16 Jan 2024 • Wasim Ahmad, Maha Shadaydeh, Joachim Denzler
Causal inference in a nonlinear system of multivariate timeseries is instrumental in disentangling the intricate web of relationships among variables, enabling us to make more accurate predictions and gain deeper insights into real-world complex systems.
no code implementations • 27 Jul 2023 • Dimitri Korsch, Maha Shadaydeh, Joachim Denzler
It utilizes the concrete dropout (CD) to sample a set of attribution masks and updates the sampling parameters based on the output of the classification model.
no code implementations • 23 Sep 2022 • Violeta Teodora Trifunov, Maha Shadaydeh, Joachim Denzler
We compare our results on synthetic data to those of a time series deconfounding method both with and without estimated confounders.
1 code implementation • 8 Jul 2022 • Wasim Ahmad, Maha Shadaydeh, Joachim Denzler
Cause-effect analysis is crucial to understand the underlying mechanism of a system.
no code implementations • 22 Sep 2021 • Wasim Ahmad, Maha Shadaydeh, Joachim Denzler
Overall our method outperforms the widely used vector autoregressive Granger causality and PCMCI in detecting nonlinear causal dependency in multivariate time series.
no code implementations • 14 Sep 2021 • Violeta Teodora Trifunov, Maha Shadaydeh, Björn Barz, Joachim Denzler
There are numerous methods for detecting anomalies in time series, but that is only the first step to understanding them.
no code implementations • 1 Feb 2021 • Oana-Iuliana Popescu, Maha Shadaydeh, Joachim Denzler
Heuristic methods result in false-positive artifacts because the image after the perturbation is far from the original data distribution.
no code implementations • 16 Dec 2020 • Maha Shadaydeh, Lea Mueller, Dana Schneider, Martin Thuemmel, Thomas Kessler, Joachim Denzler
Identifying the direction of emotional influence in a dyadic dialogue is of increasing interest in the psychological sciences with applications in psychotherapy, analysis of political interactions, or interpersonal conflict behavior.
no code implementations • 29 Oct 2018 • Lea Müller, Maha Shadaydeh, Martin Thümmel, Thomas Kessler, Dana Schneider, Joachim Denzler
Human nonverbal emotional communication in dyadic dialogs is a process of mutual influence and adaptation.
no code implementations • 9 Sep 2016 • Muzammil Behzad, Mudassir Masood, Tarig Ballal, Maha Shadaydeh, Tareq Y. Al-Naffouri
For sparse reconstruction, the likelihood of a tap being active in a patch is computed and refined through a collaboration process with other similar patches in the same group.