Tobias Huber M.Sc.

Wissenschaftlicher Mitarbeiter
Lehrstuhl für Multimodale Mensch-Technik Interaktion
Telefon: (+49)(0)821 – 598 2336
E-Mail:
Raum: 2015 (N)
Adresse: Universitätsstraße 6a, 86159 Augsburg

Forschungsinteressen

Machine Learning, Explainable Artificial Intelligence, Interpretable Machine Learning, Explainable Reinforcement Learning, Deep Reinforcement Learning, Interactive Reinforcement Learning, Human Computer Interaction

Publikationen

Tobias Huber
2020 | 2019 | 2018

2020

Katharina Weitz, Dominik Schiller, Ruben Schlagowski, Tobias Huber and Elisabeth André. in press. "Let me explain!": exploring the potential of virtual agents in explainable AI interaction design. DOI: 10.1007/s12193-020-00332-0
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Tobias Huber, Katharina Weitz, Elisabeth André and Ofra Amir. 2020. Local and global explanations of agent behavior: integrating strategy summaries with saliency maps.

Dominik Schiller, Tobias Huber, Michael Dietz and Elisabeth André. 2020. Relevance-based data masking: a model-agnostic transfer learning approach for facial expression recognition. DOI: 10.3389/fcomp.2020.00006
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Klaus Weber, Lukas Tinnes, Tobias Huber, Alexander Heimerl, Eva Pohlen, Marc-Leon Reinecker and Elisabeth André. 2020. Towards demystifying subliminal persuasiveness: using XAI-techniques to highlight persuasive markers of public speeches. DOI: 10.1007/978-3-030-51924-7_7
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2019

Katharina Weitz, Dominik Schiller, Ruben Schlagowski, Tobias Huber and Elisabeth André. 2019. "Do you trust me?" Increasing user-trust by integrating virtual agents in explainable AI interaction design. DOI: 10.1145/3308532.3329441
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Tobias Huber, Dominik Schiller and Elisabeth André. 2019. Enhancing explainability of deep reinforcement learning through selective layer-wise relevance propagation. DOI: 10.1007/978-3-030-30179-8_16
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Stanislava Rangelova, Simon Flutura, Tobias Huber, Daniel Motus and Elisabeth André. 2019. Exploration of physiological signals using different locomotion techniques in a VR adventure game. DOI: 10.1007/978-3-030-23560-4_44
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Dominik Schiller, Tobias Huber, Florian Lingenfelser, Michael Dietz, Andreas Seiderer and Elisabeth André. 2019. Relevance-based feature masking: improving neural network based whale classification through explainable artificial intelligence. DOI: 10.21437/interspeech.2019-2707
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2018

Tobias Huber. 2018. Tiefes bestärkendes Lernen: Grundlagen, Approximationseigenschaft und Implementierung multimodaler Erklärungen.
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Lehre

(Angewandte Filter: Semester: aktuelles | Institution: Multimodale Mensch-Technik-Interaktion | Dozenten: Tobias Huber | Vorlesungsarten: alle)
Name Semester Typ
Praktikum Spieleprogrammierung Wintersemester 2020/21 Praktikum

Abschlussarbeiten

  • Implementation and Comparison of Occlusion-based Explainable Artificial Intelligence Methods (BA, Benedikt Limmer, 2020)
  • Implementierung und Vergleich verschiedener Salienz-Karten Algorithmen für tiefes bestärkendes Lernen (MA, Patrick Bergmoser, 2019)

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