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Patrick Zschech
Person information
- affiliation: Leipzig University, Germany
- affiliation (former): University of Erlangen-Nuremberg, Germany
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2020 – today
- 2024
- [j16]Stefan Feuerriegel
, Jochen Hartmann
Generative AI. Bus. Inf. Syst. Eng. 66(1): 111-126 (2024) - [j15]Mathias Kraus
Interpretable generalized additive neural networks. Eur. J. Oper. Res. 317(2): 303-316 (2024) - [j14]Moritz Drobnitzky, Jonas Friederich
Survey and systematization of 3D object detection models and methods. Vis. Comput. 40(3): 1867-1913 (2024) - [c25]Theodor Felix Stoecker, Nico Hambauer, Patrick Zschech, Mathias Kraus:
IGANN Sparse: Bridging Sparsity and Interpretability with Non-Linear Insight. ECIS 2024 - [c24]Sven Weinzierl, Sandra Zilker, Patrick Zschech, Mathias Kraus, Tobias Leibelt, Martin Matzner:
How Risky is my AI System? A Method for Transparent Classification of AI System Descriptions by Regulated AI Risk Categories. ICIS 2024 - [i14]Theodor Stoecker, Nico Hambauer, Patrick Zschech
IGANN Sparse: Bridging Sparsity and Interpretability with Non-linear Insight. CoRR abs/2403.11363 (2024) - [i13]Sandra Zilker, Sven Weinzierl, Mathias Kraus, Patrick Zschech
A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis. CoRR abs/2405.13187 (2024) - [i12]Sven Kruschel, Nico Hambauer, Sven Weinzierl, Sandra Zilker, Mathias Kraus, Patrick Zschech:
Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models. CoRR abs/2409.14429 (2024) - [i11]Sven Kruschel, Lasse Bohlen, Julian Rosenberger, Patrick Zschech, Mathias Kraus:
Quantifying Visual Properties of GAM Shape Plots: Impact on Perceived Cognitive Load and Interpretability. CoRR abs/2409.16870 (2024) - 2023
- [j13]Justus Zipfel, Felix Verworner, Marco Fischer, Uwe Wieland, Mathias Kraus
Anomaly detection for industrial quality assurance: A comparative evaluation of unsupervised deep learning models. Comput. Ind. Eng. 177: 109045 (2023) - [j12]Patrick Zschech
Beyond descriptive taxonomies in data analytics: a systematic evaluation approach for data-driven method pipelines. Inf. Syst. E Bus. Manag. 21(1): 193-227 (2023) - [c23]Christopher Wissuchek, Patrick Zschech:
Survey and Systematization of Prescriptive Analytics Systems: Towards Archetypes from a Human-Machine-Collaboration Perspective. ECIS 2023 - [c22]Sandra Zilker, Sven Weinzierl, Patrick Zschech, Mathias Kraus, Martin Matzner:
Best of Both Worlds: Combining Predictive Power with Interpretable and Explainable Results for Patient Pathway Prediction. ECIS 2023 - [i10]Stefan Feuerriegel, Jochen Hartmann, Christian Janiesch, Patrick Zschech
Generative AI. CoRR abs/2309.07930 (2023) - 2022
- [j11]Philipp Siebers, Christian Janiesch
A Survey of Text Representation Methods and Their Genealogy. IEEE Access 10: 96492-96513 (2022) - [j10]Thorsten Schoormann, Gero Strobel, Frederik Möller, Dimitri Petrik, Patrick Zschech:
Artificial Intelligence for Sustainability - A Systematic Review of Information Systems Literature. Commun. Assoc. Inf. Syst. 52: 8 (2022) - [c21]Johannes Graf, Gino Lancho, Patrick Zschech, Kai Heinrich:
Where was COVID-19 first discovered? Designing a question-answering system for pandemic situations. ECIS 2022 - [c20]Patrick Zschech, Sven Weinzierl, Nico Hambauer, Sandra Zilker, Mathias Kraus:
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints. ECIS 2022 - [c19]Maximilian V. Harl, Marvin Herchenbach, Sven Kruschel, Nico Hambauer, Patrick Zschech, Mathias Kraus:
A Light in the Dark: Deep Learning Practices for Industrial Computer Vision. Wirtschaftsinformatik 2022 - [i9]Maximilian Harl, Marvin Herchenbach, Sven Kruschel, Nico Hambauer, Patrick Zschech, Mathias Kraus:
A Light in the Dark: Deep Learning Practices for Industrial Computer Vision. CoRR abs/2201.02028 (2022) - [i8]Moritz Drobnitzky, Jonas Friederich, Bernhard Egger, Patrick Zschech:
Survey and Systematization of 3D Object Detection Models and Methods. CoRR abs/2201.09354 (2022) - [i7]Johannes Graf, Gino Lancho, Patrick Zschech
Where Was COVID-19 First Discovered? Designing a Question-Answering System for Pandemic Situations. CoRR abs/2204.08787 (2022) - [i6]Patrick Zschech
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints. CoRR abs/2204.09123 (2022) - [i5]Philipp Siebers, Christian Janiesch, Patrick Zschech
A Survey of Text Representation Methods and Their Genealogy. CoRR abs/2211.14591 (2022) - 2021
- [j9]Kai Heinrich
Process data properties matter: Introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning. Decis. Support Syst. 143: 113494 (2021) - [j8]Christian Janiesch
Machine learning and deep learning. Electron. Mark. 31(3): 685-695 (2021) - [j7]Patrick Zschech
Mit Computer Vision zur automatisierten Qualitätssicherung in der industriellen Fertigung: Eine Fallstudie zur Klassifizierung von Fehlern in Solarzellen mittels Elektrolumineszenz-Bildern. HMD Prax. Wirtsch. 58(2): 321-342 (2021) - [c18]Patrick Zschech, Jannis Walk, Kai Heinrich, Michael Vössing, Niklas Kühl:
A Picture is Worth a Collaboration: Accumulating Design Knowledge for Computer-Vision-based Hybrid Intelligence Systems. ECIS 2021 - [c17]Dimitri Petrik, Katharina Pantow, Patrick Zschech, Georg Herzwurm:
Tweeting in IIoT Ecosystems - Empirical Insights from Social Media Analytics about IIoT Platforms. Software Management 2021: 21-38 - [c16]Dimitri Petrik, Katharina Pantow, Patrick Zschech, Georg Herzwurm:
Tweeting in IIoT Ecosystems - Empirical Insights from Social Media Analytics about IIoT Platforms. Wirtschaftsinformatik 2021 - [i4]Christoph Sager
labelCloud: A Lightweight Domain-Independent Labeling Tool for 3D Object Detection in Point Clouds. CoRR abs/2103.04970 (2021) - [i3]Christian Janiesch, Patrick Zschech, Kai Heinrich:
Machine learning and deep learning. CoRR abs/2104.05314 (2021) - [i2]Christoph Sager, Christian Janiesch, Patrick Zschech:
A survey of image labelling for computer vision applications. CoRR abs/2104.08885 (2021) - [i1]Patrick Zschech, Jannis Walk, Kai Heinrich, Michael Vössing, Niklas Kühl:
A Picture is Worth a Collaboration: Accumulating Design Knowledge for Computer-Vision-based Hybrid Intelligence Systems. CoRR abs/2104.11600 (2021) - 2020
- [j6]Patrick Zschech
Intelligent User Assistance for Automated Data Mining Method Selection. Bus. Inf. Syst. Eng. 62(3): 227-247 (2020) - [c15]Kai Heinrich, Johannes Graf, Ji Chen, Jakob Laurisch, Patrick Zschech:
Fool me Once, shame on You, Fool me Twice, shame on me: a Taxonomy of Attack and de-Fense Patterns for AI Security. ECIS 2020 - [c14]Jonas Wanner, Lukas-Valentin Herm, Kai Heinrich, Christian Janiesch, Patrick Zschech:
White, Grey, Black: Effects of XAI Augmentation on the Confidence in AI-based Decision Support Systems. ICIS 2020 - [c13]Jonas Wanner, Kai Heinrich, Christian Janiesch, Patrick Zschech:
How Much AI Do You Require? Decision Factors for Adopting AI Technology. ICIS 2020 - [c12]Kai Heinrich, Patrick Zschech
Ein Vergleich aktueller Deep-Learning-Architekturen zur Prognose von Prozessverhalten. Wirtschaftsinformatik (Zentrale Tracks) 2020: 876-892 - [c11]Jonas Friederich
Review and Systematization of Solutions for 3D Object Detection. Wirtschaftsinformatik (Zentrale Tracks) 2020: 1699-1711
2010 – 2019
- 2019
- [j5]Patrick Zschech
Prognostic Model Development with Missing Labels - A Condition-Based Maintenance Approach Using Machine Learning. Bus. Inf. Syst. Eng. 61(3): 327-343 (2019) - [j4]Kai Heinrich, Patrick Zschech
Objekterkennung im Weinanbau - Eine Fallstudie zur Unterstützung von Winzertätigkeiten mithilfe von Deep Learning. HMD Prax. Wirtsch. 56(5): 964-985 (2019) - [c10]Kai Heinrich, Patrick Zschech, Tarek Skouti, Jakob Griebenow, Sebastian Riechert:
Demystifying the Black Box: A Classification Scheme for Interpretation and Visualization of Deep Intelligent Systems. AMCIS 2019 - [c9]Patrick Zschech, Kai Heinrich, Richard Horn, Daniel Höschele:
Towards a Text-based Recommender System for Data Mining Method Selection. AMCIS 2019 - [c8]Kai Heinrich, Andreas Roth, Patrick Zschech:
Everything counts: a Taxonomy of Deep Learning Approaches for Object Counting. ECIS 2019 - [c7]Patrick Zschech, Jonas Bernien, Kai Heinrich:
Towards a Taxonomic Benchmarking Framework for Predictive Maintenance: The Case of NASA's Turbofan Degradation. ICIS 2019 - [c6]Richard Horn, Patrick Zschech:
Application of Process Mining Techniques to Support Maintenance-Related Objectives. Wirtschaftsinformatik 2019: 1856-1867 - 2018
- [j3]Patrick Zschech
Data Science Skills and Enabling Enterprise Systems. HMD Prax. Wirtsch. 55(1): 163-181 (2018) - [j2]Raphael Bink, Patrick Zschech
Predictive Maintenance in der industriellen Praxis. HMD Prax. Wirtsch. 55(3): 552-565 (2018) - [c5]Michael Könning, Kai Heinrich, Patrick Zschech, Christian Leyh:
Analyzing Influences on Pivotal ITO Contract Features: A Quantitative Multi-Study Design with Evidence from Western Europe. AMCIS 2018 - [c4]Karolin Stefani, Patrick Zschech:
Constituent Elements for Prescriptive Analytics Systems. ECIS 2018: 39 - [c3]Patrick Zschech:
A Taxonomy of Recurring Data Analysis Problems in Maintenance Analytics. ECIS 2018: 197 - 2017
- [c2]Patrick Zschech, Kai Heinrich, Marcus Pfitzner, Andreas Hilbert:
Are You up for the Challenge? Towards the Development of a Big Data Capability Assessment Model. ECIS 2017 - 2016
- [j1]Conny Schumann, Patrick Zschech
Das aufstrebende Berufsbild des Data Scientist. HMD Prax. Wirtsch. 53(4): 453-466 (2016) - 2014
- [c1]Paul Kruse, Christian Kummer, Patrick Zschech:
Existieren Wissensmanagement-Schulen? Eine Clusteranalyse von Wissensmanagement-Beiträgen aus den letzten 10 Jahren. GeNeMe 2014: 17-32
Coauthor Index
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last updated on 2024-11-21 20:30 CET by the dblp team
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