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Machine Learning

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Böhmer, W., Springenberg, J. T., Boedecker, J., Riedmiller, M. and Obermayer, K. (2015). Autonomous Learning of State Representations for Control: An Emerging Field Aims to Autonomously Learn State Representations for Reinforcement Learning Agents from Their Real-World Sensor Observations. Künstliche Intelligenz. Springer Berlin Heidelberg, 353-362.,10.1007/s13218-015-0356-1

Böhmer, W. and Obermayer, K. (2015). Regression with Linear Factored Functions. Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 119-134.,10.1007/978-3-319-23528-8_8

Shelton, J. A., Sheikh, A.-S., Bornschein, J., Sterne, P. and Lücke, J. (2015). Nonlinear Spike-And-Slab Sparse Coding for Interpretable Image Encoding. PLoS ONE, 10, e0124088.

Seo, S., Mohr, J., Ningfei, L., Horn, A. and Obermayer, K. (2015). Incremental pairwise clustering for large proximity matrices. 2015 International Joint Conference on Neural Networks (IJCNN), 1-8.,10.1109/IJCNN.2015.7280637


Boehmer, W., Guo, R. and Obermayer, K. (2016). Non-deterministic Policy Improvement Stabilizes Approximate Reinforcement Learning. Proceedings of the 13th European Workshop on Reinforcement Learning


Trowitzsch, I., Mohr, J., Kashef, Y. and Obermayer, K. (2017). Robust Detection of Environmental Sounds in Binaural Auditory Scenes. IEEE Transactions on Audio Speech and Language Processing, 25, 1344-1356.


Liu, C., Xie, S., Xie, X., Duan, X., Wang, W. and Obermayer, K. (2018). Design of a Video Feedback SSVEP-BCI System for Car Control Based on the Improved MUSIC Method. Proceedings of the IEEE 6th International Winter Conference on Brain-Computer Interfaces


Trowitzsch I., Schymura C., Kolossa D. and K., O. (2019). Joining Sound Event Detection and Localization Through Spatial Segregation. IEEE Trans. Audio Speech Language Proc.

Laschos, V., Obermayer, K., Shen, Y. and Stannat, W. (2019). A Fenchel-Moreau-Rockafellar type theorem on the Kantorovich-Wasserstein space with applications in partially observable Markov decision processes. Journal of Mathematical Analysis and Applications


Goerttler, T. and Obermayer, K. (2021). Exploring the Similarity of Representations in Model-Agnostic Meta-Learning. Learning to Learn workshop at ICLR 2021

Müller, L., Ploner, M., Goerttler, T. and Obermayer, K. (2021). An Interactive Introduction to Model-Agnostic Meta-Learning. Workshop on Visualization for AI Explainability at IEEE VIS

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