Artificial

Intelligence

Lab

For additional information and material about our publications,
visit https://bib.sebastianstober.de

2022

• Visualizing Deep Neural Networks with Topographic Activation Maps.
  Andreas Krug; Raihan Kabir Ratul & Sebastian Stober.
  In: arXiv preprint arXiv:2204.03528, 2022.
  [PDF] [github]

2021

• Hierarchical Predictive Coding and Interpretable Audio Analysis-Synthesis.
  André Ofner; Johannes Schleiss & Sebastian Stober.
  In: Proc. of the 15th International Symposium on CMMR, 2021.
  [PDF]
  
  
• Uncertainty-aware temporal self-learning (UATS) - semi-supervised learning for segmentation of prostate zones and beyond.
  Anneke Meyer; Suhita Ghosh; Daniel Schindele; Martin Schostak; Sebastian Stober; Christian Hansen & Marko Rak.
  In: Artificial intelligence in medicine: AIM - Amsterdam [u.a.]: Elsevier Science - AIM, Bd. 116, 2021.
  [PDF]
  
  
• Analyzing and Visualizing Deep Neural Networks for Speech Recognition with Saliency-Adjusted Neuron Activation Profiles.
  Andreas Krug; Maral Ebrahimzadeh; Jost Alemann; Jens Johannsmeier & Sebastian Stober.
  In: Electronics 10 (11), 1350, 2021.
  [URL]
  
  
• Perceptual Losses Facilitate CT Denoising and Artifact Removal.
  Suhita Ghosh; Andreas Krug; Georg Rose & Sebastian Stober.
  In: 2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS). IEEE, 2021.
  [URL]
  
  

• Distributed Planning with Active Inference.
  André Ofner; Johannes Schleiss & Sebastian Stober.
  In: Bernstein Conference, 2021.
  [URL]
  
  
• Visualizing Artificial Neural Network Activations as Topographic Maps.
  Andreas Krug; Raihan Kabir Ratul & Sebastian Stober.
  In: Bernstein Conference, 2021.
  [URL]
  
  
• Few-Shot Bioacoustic Event Detection via Segmentation using Prototypical Networks.
  Jens Johannsmeier & Sebastian Stober.
  In: Detection and Classification of Acoustic Scenes and Events (DCASE), 2021.
  [PDF]
  
  
• Approaching Scheduling Problems via a Deep Hybrid Greedy Model and Supervised Learning.
  Johann Schmidt & Sebastian Stober.
  In: IFAC-PapersOnline 54 (1), 805-810, 2021.
  [URL]

2020

• Automatic prostate and prostate zones segmentation of magnetic resonance images using DenseNet-like U-net.
  Nader Aldoj; Federico Biavati; Florian Michallek; Sebastian Stober & Marc Dewey.
  In: Scientific Reports, Volume 10, Number 1, Springer Science and Business Media LLC, 2020.
  [DOI] [PDF]
  
  
• Balancing Active Inference and Active Learning with Deep Variational Predictive Coding for EEG.
  André Ofner & Sebastian Stober.
  In: IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020), 2020.
  [URL]
  
  
• Modeling perception with hierarchical prediction: Auditory segmentation with deep predictive coding locates candidate evoked potentials in EEG.
  André Ofner & Sebastian Stober.
  In: ResearchGATE: scientific neetwork ; the leading professional network for scientists - Cambridge, Mass.: ResearchGATE Corp., 2010, 2020.
  [URL] [PDF]
  
  
• Applying deep learning to single-trial EEG data provides evidence for complementary theories on action control.
  Amirali Vahid; Moritz Mückschel; Sebastian Stober: Ann-Kathrin Stock & Christian Beste.
  In: Communications biology - London: Springer Nature, Vol. 3.2020, Art.-Nr. 112, 11, 2020.
  [DOI] [PDF]
  
  
• Analyzing regions of safety for handling shared data in cooperative systems.
  Georg Jäger; Johannes Schleiss; Sasiporn Usanavasin; Sebastian Stober & Sebastian Zug.
  In: 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2020.
  [URL]
  
  
• PredNet and Predictive Coding: A Critical Review.
  Roshan Prakash Rane; Edit Szügyi; Vageesh Saxena; André Ofner & Sebastian Stober.
  In: Proceedings of the 2020 International Conference on Multimedia Retrieval, ICMR ’20, Pages 233–241, Association for Computing Machinery, New York, NY, USA, 2020.
  [DOI] [PDF]
  
  

• Exploration of interpretability techniques for deep COVID-19 classification using chest X-ray images.
  Soumick Chatterjee; Fatima Saad; Chompunuch Sarasaen; Suhita Ghosh; Rupali Khatun; Petia Radeva; Georg Rose; Sebastian Stober; Oliver Speck & Andreas Nürnberger.
  In: arXiv preprint arXiv:2006.02570, 2020.
  [PDF]
  
  
• Gradient-adjusted neuron activation profiles for comprehensive introspection of convolutional speech recognition models.
  Andreas Krug & Sebastian Stober.
  In: arXiv preprint arXiv:2002.08125, 2020.
  [PDF]

2019

• Window-Based Neural Tagging for Shallow Discourse Argument Labeling.
  René Knaebel; Manfred Stede & Sebastian Stober.
  In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), Pages 768-777, 2019.
  [DOI] [PDF]
  
  
• The ISMIR Explorer – A Visual Interface for Exploring 20 Years of ISMIR Publications.
  Thomas Low; Christian Hentschel; Sayantan Polley; Anustup Das; Harald Sack; Andreas Nürnberger & Sebastian Stober.
  In: 20th International Society for Music Information Retrieval Conference (ISMIR’19), Pages 392-399, 2019.
  [PDF]
  
  
• Predictive Coding Based Vision For Autonomous Cars.
  Roshan Prakash Rane;​ André Ofner; Shreyas Gite​ & Sebastian Stober.
  In: Computational Cognition 2019 Workshop, 2019.
  [URL]
  
  
• Knowledge transfer in coupled predictive coding networks.
  André Ofner & Sebastian Stober.
  In: Bernstein Conference, 2019.
  [DOI]
  
  
• Visualizing Deep Neural Networks for Speech Recognition with Learned Topographic Filter Maps.
  Andreas Krug & Sebastian Stober.
  In: Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, 2019.
  [PDF]
  
  
• Siri visualisiert.
  Andreas Krug & Sebastian Stober.
  In: Proceedings of the 2019 NaWik Symposium Karlsruhe, Pages 24-25, 2019.
  
  
• Deep Learning Based on Event-Related EEG Differentiates Children with ADHD from Healthy Controls.
  Amirali Vahid; Annet Bluschke; Veit Roessner; Sebastian Stober & Christian Beste.
  In: Journal of Clinical Medicine, Volume 8, Number 7, 2019.
  [DOI] [PDF]
  
  
• Hybrid Variational Predictive Coding as a Bridge between Human and Artificial Cognition.
  André Ofner & Sebastian Stober.
  In: The 2019 Conference on Artificial Life, Number 31, Pages 68-69, 2019.
  [URL] [PDF]
  
  
• Automatic prostate and prostate zones segmentation of magnetic resonance images using convolutional neural networks.
  Nader Aldoj; Federico Biavati; Miriam Rutz; Florian Michallek; Sebastian Stober & Marc Dewey.
  In: Proceedings of International Conference on Medical Imaging with Deep Learning (MIDL’19), 2019.
  [DOI] [PDF]