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Machine Learning

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A

Adiloglu, K., Annies, R., Henrich, F., Paus, A. and Obermayer, K. (2008). Geometrical Approaches to Active Learning [9]. 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 [10]. 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 [11]. 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 [12]. Machine Learning, 89, 67–86.


Burger, M., Graepel, T. and Obermayer, K. (1997). Phase Transitions in Soft Topographic Vector Quantization [13]. 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 [14]. 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 [15]. 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 [16]. Journal of Machine Learning Research, 14, 2067–2118.


Böhmer, W. and Obermayer, K. (2015). Regression with Linear Factored Functions [17]. 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 [18]. 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 [19]. Proceedings WSOM, 167 – 172.,


D

Dai, Z. and Lücke, J. (2014). Autonomous Document Cleaning – A Generative Approach to Reconstruct Strongly Corrupted Scanned Texts [20]. 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 [21]. 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 [22]. Biological Cybernetics, 67, 47 – 55.


Erwin, E., Obermayer, K. and Schulten, K. (1992). Self-Organizing Maps: Stationary States, Metastability and Convergence Rate [23]. Biological Cybernetics, 67, 35 – 45.


G

Goerttler, T. and Obermayer, K. (2021). Exploring the Similarity of Representations in Model-Agnostic Meta-Learning [24]. 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 [25]. 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 [26]. Neurocomputing, 20, 173 – 190.


Graepel, T., Burger, M. and Obermayer, K. (1997). Phase Transitions in Stochastic Self-Organizing Maps [27]. PHYSICAL REVIEW E, 56, 3876 – 3890.


Graepel, T., Herbrich, R., Bollmann-Sdorra, P. and Obermayer, K. (1999). Classification on Pairwise Proximity Data [28]. Advances in Neural Information Processing Systems 11. MIT Press, 438 – 444.,


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