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Machine Learning

All Publications

A

Adiloglu, K., Annies, R., Henrich, F., Paus, A. and Obermayer, K. (2008). Geometrical Approaches to Active Learning. Autonomous Systems – Self-Organization, Management, and Control. Springer Netherlands, 11-19.,10.1007/978-1-4020-8889-6_2


B

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


Böhmer, W., Grünewälder, S., Nickisch, H. and Obermayer, K. (2011). Regularized Sparse Kernel Slow Feature Analysis. Lecture Notes in Computer Science. Springer-Verlag Berlin Heidelberg, 235–248.,


Böhmer, W., Grünewalder, S., Nickisch, H. and Obermayer, K. (2012). Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis. Machine Learning, 89, 67–86.


Burger, M., Graepel, T. and Obermayer, K. (1997). Phase Transitions in Soft Topographic Vector Quantization. Artificial Neural Networks - ICANN 97. Springer-Verlag, 619 – 624.,


Burger, M. a. G. T. and Obermayer, K. (1998). An Annealed Self-Organizing Map for Source Channel Coding. Advances in Neural Information Processing Systems 10. MIT Press, 430 – 436.,10.1.1.26.9359


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., Grünewälder, S., Shen, Y., Musial, M. and Obermayer, K. (2013). Construction of Approximation Spaces for Reinforcement Learning. Journal of Machine Learning Research, 14, 2067–2118.


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


Böhmer, W. and Obermayer, K. (2013). Towards Structural Generalization: Factored Approximate Planning. ICRA Workshop on Autonomous Learning


C

Cuadros-Vargas, E., Romero, R. and Obermayer, K. (2003). Speeding up Algorithms of the SOM Family for Large and High Dimensional Databases. Proceedings WSOM, 167 – 172.,


D

Dai, Z. and Lücke, J. (2014). Autonomous Document Cleaning – A Generative Approach to Reconstruct Strongly Corrupted Scanned Texts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36, 1950–1962.


E

Erwin, E., Obermayer, K. and Schulten, K. (1991). Convergence Properties of Self-organizing Maps. Artificial Neural Networks I. North Holland, 409 – 414.,


Erwin, E., Obermayer, K. and Schulten, K. (1992). Self-Organizing Maps: Ordering, Convergence Properties and Energy Functions. Biological Cybernetics, 67, 47 – 55.


Erwin, E., Obermayer, K. and Schulten, K. (1992). Self-Organizing Maps: Stationary States, Metastability and Convergence Rate. Biological Cybernetics, 67, 35 – 45.


G

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


Graepel, T., Burger, M. and Obermayer, K. (1997). Deterministic Annealing for Topographic Vector Quantization and Self-Organizing Maps. Proceedings of the Workshop on Self-Organizing Maps - WSOM 97, 345 – 350.,


Graepel, T., Burger, M. and Obermayer, K. (1998). Self-Organizing Maps: Generalizations and New Optimization Techniques. Neurocomputing, 20, 173 – 190.


Graepel, T., Burger, M. and Obermayer, K. (1997). Phase Transitions in Stochastic Self-Organizing Maps. PHYSICAL REVIEW E, 56, 3876 – 3890.


Graepel, T., Herbrich, R., Bollmann-Sdorra, P. and Obermayer, K. (1999). Classification on Pairwise Proximity Data. Advances in Neural Information Processing Systems 11. MIT Press, 438 – 444.,


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