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Inhalt des Dokuments

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Garg, A., Bhaumik, B. and Obermayer, K. (2005). Variable Discharge Pattern and Contrast Invariant Orientation Tuning of a Simple Cell: A Modeling Study. Neural Information Processing - Letters and Reviews, 6, 59 – 68.



Garg, A., Bhaumik, B. and Obermayer, K. (2004). Development of a Simple Cell Preceptive Field Structure: A Model Based on Hetero-Synaptic Interactions. Lecture Notes in Computer Science. Springer-Verlag, 64 – 68.,


Garg, A., Obermayer, K. and Bhaumik, B. (2005). Development of Feedforward Receptive Field Structure of a Simple Cell and its Contribution to the Orientation Selectivity: A Modeling Study. International Journal of Neural Systems, 15, 55 – 70.


Gaudnek, M., Hess, A., Obermayer, K., Budinsky, L., Brune, K. and Sibila, M. (2005). Geometric Reconstruction of the Rat Vascular System Imaged by MRA. IEEE International Conference on Image Processing 2005. IEEE, 1278-1281.,10.1109/ICIP.2005.1530296


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.,


Graepel, T., Herbrich, R. and Obermayer, K. (2000). Bayesian Transduction. Advances in Neural Information Processing Systems 12. MIT Press, 456 – 462.,


Graepel, T., Herbrich, R. and Obermayer, K. (1999). Bayesian transductive classification by maximizing volume in version space. Proceedings of Learning 1999 Conference,


Graepel, T., Herbrich, R., Schoelkopf, B., Smola, A., Bartlett, P., Mueller, K., Obermayer, K. and Williamson, R. (1999). Classification on Proximity Data with LP-Machines. 9th International Conference on Artificial Neural Networks - ICANN99. IEEE, 304 – 309.,10.1049/cp:19991126


Graepel, T. and Obermayer, K. (1998). Fuzzy Topographic Kernel Clustering. Proceedings of the 5th GI Workshop Fuzzy Neuro Systems, 90 – 97.,


Graepel, T. and Obermayer, K. (1999). A Self-Organizing Map for Proximity Data. Neural Computation, 11, 139 – 155.


Grünwälder, S. and Obermayer, K. (2011). The Optimal Unbiased Extimator and its Relation to LSTD, TD and MC. Machine Learning, 83, 289 – 330.


Grünewälder, S. and Obermayer, K. (2007). Optimality of LSTD and its Relation to MC. Neural Networks, IJCNN 2007, 338 – 343.,


Grünewälder, S. and Obermayer, K. (2005). Attention Driven Memory. Proceedings of the 27th Annual Conf. of the Cognitive Science Society, 845 – 850.,


Guggenmos, M., Rothkirch, M., Obermayer, K., Haynes, J. D. and Sterzer, P. (2015). A Hippocampal Signature of Perceptual Learning in Object Recognition. Journal of Cognitive Neuroscience, 27, 787–797.


Guo, R., Böhmer, W., Hebart, M., Chien, S., Sommer, T., Obermayer, K. and Gläscher, J. (2016). Interaction of Instrumental and Goal-directed Learning Modulates Prediction Error Representations in the Ventral Striatum. Journal of Neuroscience, 36, 12650-12660.


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