Wolfgang Stammer

According to our database, Wolfgang Stammer authored at least 22 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Human-in-the-loop or AI-in-the-loop? Automate or Collaborate?

Sriraam Natarajan, Saurabh Mathur, Sahil Sidheekh, Wolfgang Stammer, Kristian Kersting

CoRR, 2024

Neural Concept Binder.

Wolfgang Stammer, Antonia Wüst, David Steinmann, Kristian Kersting

CoRR, 2024

Pix2Code: Learning to Compose Neural Visual Concepts as Programs.

Antonia Wüst, Wolfgang Stammer, Quentin Delfosse, Devendra Singh Dhami, Kristian Kersting

CoRR, 2024

Where is the Truth? The Risk of Getting Confounded in a Continual World.

Florian Peter Busch, Roshni Kamath, Rupert Mitchell, Wolfgang Stammer, Kristian Kersting, Martin Mundt

CoRR, 2024

Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents.

Quentin Delfosse, Sebastian Sztwiertnia, Wolfgang Stammer, Mark Rothermel, Kristian Kersting

CoRR, 2024

Learning to Intervene on Concept Bottlenecks.

David Steinmann, Wolfgang Stammer, Felix Friedrich, Kristian Kersting

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Explanatory Interactive Machine Learning.

Nicolas Pfeuffer, Lorenz Baum, Wolfgang Stammer, Benjamin M. Abdel-Karim, Patrick Schramowski, Andreas M. Bucher, Christian Hügel, Gernot Rohde, Kristian Kersting, Oliver Hinz

Bus. Inf. Syst. Eng., December, 2023

A typology for exploring the mitigation of shortcut behaviour.

Felix Friedrich, Wolfgang Stammer, Patrick Schramowski, Kristian Kersting

Nat. Mac. Intell., March, 2023

Leveraging explanations in interactive machine learning: An overview.

Stefano Teso, Öznur Alkan, Wolfgang Stammer, Elizabeth Daly

Frontiers Artif. Intell., February, 2023

Learning by Self-Explaining.

Wolfgang Stammer, Felix Friedrich, David Steinmann, Hikaru Shindo, Kristian Kersting

CoRR, 2023

V-LoL: A Diagnostic Dataset for Visual Logical Learning.

Lukas Helff, Wolfgang Stammer, Hikaru Shindo, Devendra Singh Dhami, Kristian Kersting

CoRR, 2023

Boosting Object Representation Learning via Motion and Object Continuity.

Quentin Delfosse, Wolfgang Stammer, Thomas Rothenbacher, Dwarak Vittal, Kristian Kersting

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Revision Transformers: Instructing Language Models to Change Their Values.

Felix Friedrich, Wolfgang Stammer, Patrick Schramowski, Kristian Kersting

Proceedings of the ECAI 2023 - 26th European Conference on Artificial Intelligence, September 30 - October 4, 2023, Kraków, Poland, 2023

2022

Revision Transformers: Getting RiT of No-Nos.

Felix Friedrich, Wolfgang Stammer, Patrick Schramowski, Kristian Kersting

CoRR, 2022

A Typology to Explore and Guide Explanatory Interactive Machine Learning.

Felix Friedrich, Wolfgang Stammer, Patrick Schramowski, Kristian Kersting

CoRR, 2022

Neural-Probabilistic Answer Set Programming.

Arseny Skryagin, Wolfgang Stammer, Daniel Ochs, Devendra Singh Dhami, Kristian Kersting

Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning, 2022

Interactive Disentanglement: Learning Concepts by Interacting with their Prototype Representations.

Wolfgang Stammer, Marius Memmel, Patrick Schramowski, Kristian Kersting

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

SLASH: Embracing Probabilistic Circuits into Neural Answer Set Programming.

Arseny Skryagin, Wolfgang Stammer, Daniel Ochs, Devendra Singh Dhami, Kristian Kersting

CoRR, 2021

Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting With Their Explanations.

Wolfgang Stammer, Patrick Schramowski, Kristian Kersting

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Right for Better Reasons: Training Differentiable Models by Constraining their Influence Functions.

Xiaoting Shao, Arseny Skryagin, Wolfgang Stammer, Patrick Schramowski, Kristian Kersting

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Making deep neural networks right for the right scientific reasons by interacting with their explanations.

Patrick Schramowski, Wolfgang Stammer, Stefano Teso, Anna Brugger, Franziska Herbert, Xiaoting Shao, Hans-Georg Luigs, Anne-Katrin Mahlein, Kristian Kersting

Nat. Mach. Intell., 2020

Right for the Wrong Scientific Reasons: Revising Deep Networks by Interacting with their Explanations.

Patrick Schramowski, Wolfgang Stammer, Stefano Teso, Anna Brugger, Xiaoting Shao, Hans-Georg Luigs, Anne-Katrin Mahlein, Kristian Kersting

CoRR, 2020