54 citations · 85 across the 19 of their papers we have counts for
19 papers
Mitigating Spurious Correlations in Patch-wise Tumor Classification on High-Resolution Multimodal Images
Ihab Asaad, Maha Shadaydeh, Joachim Denzler
Patch-wise multi-label classification provides an efficient alternative to full pixel-wise segmentation on high-resolution images, particularly when the objective is to determine t…
Group Interventions on Deep Networks for Causal Discovery in Subsystems
Wasim Ahmad, Joachim Denzler, Maha Shadaydeh
Causal discovery uncovers complex relationships between variables, enhancing predictions, decision-making, and insights into real-world systems, especially in nonlinear multivariat…
Gradient Extrapolation for Debiased Representation Learning
Ihab Asaad, Maha Shadaydeh, Joachim Denzler
Machine learning classification models trained with empirical risk minimization (ERM) often inadvertently rely on spurious correlations. When absent in the test data, these uninten…
Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-regenerative Life Support System Telemetry
Ferdinand Rewicki, Jakob Gawlikowski, Julia Niebling +1
The detection of abnormal or critical system states is essential in condition monitoring. While much attention is given to promptly identifying anomalies, a retrospective analysis…
Regime Identification for Improving Causal Analysis in Non-stationary Timeseries
Wasim Ahmad, Maha Shadaydeh, Joachim Denzler
Time series data from real-world systems often display non-stationary behavior, indicating varying statistical characteristics over time. This inherent variability poses significan…
Physics Informed Modeling of Ecosystem Respiration via Dynamic Mode Decomposition with Control Input
Maha Shadaydeh, Joachim Denzler, Mirco Migliavacca
Ecosystem respiration (Reco) represents a major component of the global carbon cycle, and accurate characterization of its dynamics is essential for a comprehensive understanding o…
Embracing the black box: Heading towards foundation models for causal discovery from time series data
Gideon Stein, Maha Shadaydeh, Joachim Denzler
Causal discovery from time series data encompasses many existing solutions, including those based on deep learning techniques. However, these methods typically do not endorse one o…
Deep Learning-based Group Causal Inference in Multivariate Time-series
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 accu…
Let's Get the FACS Straight -- Reconstructing Obstructed Facial Features
Tim Büchner, Sven Sickert, Gerd Fabian Volk +3
The human face is one of the most crucial parts in interhuman communication. Even when parts of the face are hidden or obstructed the underlying facial movements can be understood.…
Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series
Ferdinand Rewicki, Joachim Denzler, Julia Niebling
Detecting anomalies in time series data is important in a variety of fields, including system monitoring, healthcare, and cybersecurity. While the abundance of available methods ma…
Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventions
Violeta Teodora Trifunov, Maha Shadaydeh, Joachim Denzler
Latent variables often mask cause-effect relationships in observational data which provokes spurious links that may be misinterpreted as causal. This problem sparks great interest…
Causal Discovery using Model Invariance through Knockoff Interventions
Wasim Ahmad, Maha Shadaydeh, Joachim Denzler
Cause-effect analysis is crucial to understand the underlying mechanism of a system. We propose to exploit model invariance through interventions on the predictors to infer causali…
Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals
Wasim Ahmad, Maha Shadaydeh, Joachim Denzler
Estimating causal relations is vital in understanding the complex interactions in multivariate time series. Non-linear coupling of variables is one of the major challenges inaccura…
Anomaly Attribution of Multivariate Time Series using Counterfactual Reasoning
Violeta Teodora Trifunov, Maha Shadaydeh, Björn Barz +1
There are numerous methods for detecting anomalies in time series, but that is only the first step to understanding them. We strive to exceed this by explaining those anomalies. Th…
EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task
Christian Requena-Mesa, Vitus Benson, Markus Reichstein +2
Satellite images are snapshots of the Earth surface. We propose to forecast them. We frame Earth surface forecasting as the task of predicting satellite imagery conditioned on futu…
Self-Supervised Learning from Semantically Imprecise Data
Clemens-Alexander Brust, Björn Barz, Joachim Denzler
Learning from imprecise labels such as "animal" or "bird", but making precise predictions like "snow bunting" at inference time is an important capability for any classifier when e…
Analysing the Direction of Emotional Influence in Nonverbal Dyadic Communication: A Facial-Expression Study
Maha Shadaydeh, Lea Mueller, Dana Schneider +3
Identifying the direction of emotional influence in a dyadic dialogue is of increasing interest in the psychological sciences with applications in psychotherapy, analysis of politi…
EarthNet2021: A novel large-scale dataset and challenge for forecasting localized climate impacts
Christian Requena-Mesa, Vitus Benson, Joachim Denzler +2
Climate change is global, yet its concrete impacts can strongly vary between different locations in the same region. Seasonal weather forecasts currently operate at the mesoscale (…
Active and Incremental Learning with Weak Supervision
Clemens-Alexander Brust, Christoph Käding, Joachim Denzler
Large amounts of labeled training data are one of the main contributors to the great success that deep models have achieved in the past. Label acquisition for tasks other than benc…