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Udo Schlegel
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2020 – today
- 2024
- [c22]Zuzana Vopálková, Jakub Yaghob, Michael Stroh, Udo Schlegel, Jakub Lokoc:
Searching Temporally Distant Activities in Lifelog Data With PraK Tool V2. LSC@ICMR 2024: 111-116 - [c21]Jakub Lokoc, Zuzana Vopálková, Michael Stroh, Raphael Buchmueller, Udo Schlegel:
PraK Tool: An Interactive Search Tool Based on Video Data Services. MMM (4) 2024: 340-346 - [i14]Udo Schlegel, Daniel A. Keim, Tobias Sutter:
Finding the DeepDream for Time Series: Activation Maximization for Univariate Time Series. CoRR abs/2408.10628 (2024) - [i13]Udo Schlegel, Julius Rauscher, Daniel A. Keim:
Interactive Counterfactual Generation for Univariate Time Series. CoRR abs/2408.10633 (2024) - [i12]Udo Schlegel, Daniel A. Keim:
Interactive dense pixel visualizations for time series and model attribution explanations. CoRR abs/2408.15073 (2024) - 2023
- [c20]Udo Schlegel, Daniel A. Keim:
Interactive Dense Pixel Visualizations for Time Series and Model Attribution Explanations. MLVis@EuroVis 2023: 1-5 - [c19]Sonia Laguna, Julian N. Heidenreich, Jiugeng Sun, Nilüfer Cetin, Ibrahim Al Hazwani, Udo Schlegel, Furui Cheng, Mennatallah El-Assady:
ExpLIMEable: A Visual Analytics Approach for Exploring LIME. VAHC 2023: 27-33 - [c18]Udo Schlegel, Daniel A. Keim:
A Deep Dive into Perturbations as Evaluation Technique for Time Series XAI. xAI (3) 2023: 165-180 - [i11]Udo Schlegel, Daniel A. Keim:
A Deep Dive into Perturbations as Evaluation Technique for Time Series XAI. CoRR abs/2307.05104 (2023) - [i10]Udo Schlegel, Daniela Oelke, Daniel A. Keim, Mennatallah El-Assady:
Visual Explanations with Attributions and Counterfactuals on Time Series Classification. CoRR abs/2307.08494 (2023) - [i9]Udo Schlegel, Daniel A. Keim:
Introducing the Attribution Stability Indicator: a Measure for Time Series XAI Attributions. CoRR abs/2310.04178 (2023) - 2022
- [j5]Andreas Theissler, Francesco Spinnato, Udo Schlegel, Riccardo Guidotti:
Explainable AI for Time Series Classification: A Review, Taxonomy and Research Directions. IEEE Access 10: 100700-100724 (2022) - [j4]Dirk Streeb, Yannick Metz, Udo Schlegel, Bruno Schneider, Mennatallah El-Assady, Hansjörg Neth, Min Chen, Daniel A. Keim:
Task-Based Visual Interactive Modeling: Decision Trees and Rule-Based Classifiers. IEEE Trans. Vis. Comput. Graph. 28(9): 3307-3323 (2022) - [c17]Yannick Metz, Udo Schlegel, Daniel Seebacher, Mennatallah El-Assady, Daniel A. Keim:
A Comprehensive Workflow for Effective Imitation and Reinforcement Learning with Visual Analytics. EuroVA@EuroVis 2022: 19-23 - [c16]Udo Schlegel, Samuel Schiegg, Daniel A. Keim:
ViNNPruner: Visual Interactive Pruning for Deep Learning. MLVis@EuroVis 2022: 13-17 - [i8]Udo Schlegel, Samuel Schiegg, Daniel A. Keim:
ViNNPruner: Visual Interactive Pruning for Deep Learning. CoRR abs/2205.15731 (2022) - 2021
- [j3]Juri F. Buchmüller, Udo Schlegel, Eren Cakmak, Daniel A. Keim, Evanthia Dimara:
SpatialRugs: A compact visualization of space and time for analyzing collective movement data. Comput. Graph. 101: 23-34 (2021) - [j2]Eren Cakmak, Udo Schlegel, Dominik Jäckle, Daniel A. Keim, Tobias Schreck:
Multiscale Snapshots: Visual Analysis of Temporal Summaries in Dynamic Graphs. IEEE Trans. Vis. Comput. Graph. 27(2): 517-527 (2021) - [c15]Thilo Spinner, Udo Schlegel, Martin Schall, Fabian Sperrle, Rita Sevastjanova, Beatrice Gobbo, Julius Rauscher, Mennatallah El-Assady, Daniel A. Keim:
Speculative Execution of Similarity Queries: Real-Time Parameter Optimization through Visual Exploration. EDBT/ICDT Workshops 2021 - [c14]Udo Schlegel, Duy Vo Lam, Daniel A. Keim, Daniel Seebacher:
TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models. PKDD/ECML Workshops (1) 2021: 5-14 - [i7]Udo Schlegel, Duy Vo Lam, Daniel A. Keim, Daniel Seebacher:
TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models. CoRR abs/2109.08438 (2021) - [i6]Udo Schlegel, Daniel A. Keim:
Time Series Model Attribution Visualizations as Explanations. CoRR abs/2109.12935 (2021) - 2020
- [j1]Thilo Spinner, Udo Schlegel, Hanna Schäfer, Mennatallah El-Assady:
explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning. IEEE Trans. Vis. Comput. Graph. 26(1): 1064-1074 (2020) - [c13]Juri F. Buchmüller, Udo Schlegel, Eren Cakmak, Daniel A. Keim, Evanthia Dimara:
SpatialRugs: Enhancing Spatial Awareness of Movement in Dense Pixel Visualizations. EuroVA@Eurographics/EuroVis 2020: 1-5 - [c12]Udo Schlegel, Eren Cakmak, Hiba Arnout, Mennatallah El-Assady, Daniela Oelke, Daniel A. Keim:
Towards visual debugging for multi-target time series classification. IUI 2020: 202-206 - [c11]Udo Schlegel, Eren Cakmak, Daniel A. Keim:
ModelSpeX: Model Specification Using Explainable Artificial Intelligence Methods. MLVis@Eurographics/EuroVis 2020: 7-11 - [i5]Juri F. Buchmüller, Udo Schlegel, Eren Cakmak, Evanthia Dimara, Daniel A. Keim:
SpatialRugs: Enhancing Spatial Awareness of Movement in Dense Pixel Visualizations. CoRR abs/2003.12282 (2020) - [i4]Eren Cakmak, Udo Schlegel, Dominik Jäckle, Daniel A. Keim, Tobias Schreck:
Multiscale Snapshots: Visual Analysis of Temporal Summaries in Dynamic Graphs. CoRR abs/2008.08282 (2020) - [i3]Udo Schlegel, Daniela Oelke, Daniel A. Keim, Mennatallah El-Assady:
An Empirical Study of Explainable AI Techniques on Deep Learning Models For Time Series Tasks. CoRR abs/2012.04344 (2020)
2010 – 2019
- 2019
- [c10]Wolfgang Jentner, Juri Buchmüller, Fabian Sperrle, Rita Sevastjanova, Thilo Spinner, Udo Schlegel, Dirk Streeb, Hanna Schäfer:
N.E.A.T. - Novel Emergency Analysis Tool. VAST 2019: 104-105 - [c9]Astrik Jeitler, Alpin Türkoglu, Denis Makarov, Timo Jockers, Juri Buchmüller, Udo Schlegel, Daniel A. Keim:
RescueMark: Visual Analytics of Social Media Data for Guiding Emergency Response in Disaster Situations: Award for Skillful Integration of Language Model. VAST 2019: 120-121 - [c8]Arjun Majumdar, Gent Ymeri, Sebastian Strumbelj, Juri Buchmüller, Udo Schlegel, Daniel A. Keim:
Earthquake Investigation and Visual Cognizance of Multivariate Temporal Tabular Data Using Machine Learning. VAST 2019: 136-137 - [c7]Fabian Nagel, Giuliano Castiglia, Gemza Ademaj, Juri Buchmüller, Udo Schlegel, Daniel A. Keim:
cpmViz: A Web-Based Visualization Tool for Uncertain Spatiotemporal Data. VAST 2019: 140-141 - [c6]Udo Schlegel, Wolfgang Jentner, Juri Buchmüller, Eren Cakmak, Giuliano Castiglia, Renzo Canepa, Simone Petralli, Luca Oneto, Daniel A. Keim, Davide Anguita:
Visual Analytics for Supporting Conflict Resolution in Large Railway Networks. INNSBDDL 2019: 206-215 - [i2]Thilo Spinner, Udo Schlegel, Hanna Schäfer, Mennatallah El-Assady:
explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning. CoRR abs/1908.00087 (2019) - [i1]Udo Schlegel, Hiba Arnout, Mennatallah El-Assady, Daniela Oelke, Daniel A. Keim:
Towards a Rigorous Evaluation of XAI Methods on Time Series. CoRR abs/1909.07082 (2019) - 2018
- [c5]Udo Schlegel, Eren Cakmak, Juri Buchmüller, Daniel A. Keim:
G-Rap: interactive text synthesis using recurrent neural network suggestions. ESANN 2018 - [c4]Eren Cakmak, Udo Schlegel, Matthias Miller, Juri Buchmüller, Wolfgang Jentner, Daniel A. Keim:
Interactive Classification Using Spectrograms and Audio Glyphs. VAST 2018: 110-111 - 2017
- [c3]Udo Schlegel, Alexandra Diehl, Daniel A. Keim:
SIZE: Satellite Image Zooming and Exploration. VAST 2017: 219-220 - [c2]Dirk Streeb, Juri Buchmüller, Udo Schlegel, Wolfgang Jentner, Michael Behrisch, Bruno Schneider, Daniel Seebacher:
Uncovering the Mistford Toxic Conspiracy. VAST 2017: 243-244 - 2015
- [c1]Dirk Streeb, Udo Schlegel, Juri Buchmüller, Fabian Fischer, Daniel A. Keim:
Using visual analytics to analyze movement and action patterns. VAST 2015: 171-172
Coauthor Index
aka: Juri F. Buchmüller
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last updated on 2024-10-07 21:23 CEST by the dblp team
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