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Shelton, J. A., Sheikh, A.-S., Bornschein, J., Sterne, P. and Lücke, J. (2015). Nonlinear Spike-And-Slab Sparse Coding for Interpretable Image Encoding [23]. PLoS ONE, 10, e0124088.

Guggenmos, M., Rothkirch, M., Obermayer, K., Haynes, J. D. and Sterzer, P. (2015). A Hippocampal Signature of Perceptual Learning in Object Recognition [24]. Journal of Cognitive Neuroscience, 27, 787–797.

Hutter, F., Lücke, J. and Schmidt-Thieme, L. (2015). Beyond Manual Tuning of Hyperparameters [25]. KI - Künstliche Intelligenz, 29, 329-337.

Kneer, F., Obermayer, K. and Dahlem, M. A. (2015). Analyzing critical propagation in a reaction-diffusion-advection model using unstable slow waves [26]. The European Physical Journal E, 38

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 [27]. Künstliche Intelligenz. Springer Berlin Heidelberg, 353-362.,10.1007/s13218-015-0356-1

Schmidt, S., Scholz, M., Haberbosch, L., Mante, A., Obermayer, K. and Brandt, S. A. (2015). P273: Fast induction of alpha entrainment with bandwidth confined electric and photic stimulation. [28]. Clinical Neurophysiology, 125, S122.

Böhmer, W. and Obermayer, K. (2015). Regression with Linear Factored Functions [29]. Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 119-134.,10.1007/978-3-319-23528-8_8

Franke, F., Pröpper, R., Alle, H., Meier, P., Geiger, J. R. P., Obermayer, K. and Munk, M. H. J. (2015). Spike sorting of synchronous spikes from local neuron ensembles [30]. Journal of Neurophysiology, 114, 2535–2549.

Franke, F., Quiroga, R. Q., Hierlemann, A. and Obermayer, K. (2015). Bayes optimal template matching for spike sorting – combining fisher discriminant analysis with optimal filtering [31]. Journal of Computational Neuroscience, 38, 439-459.


Mohr, J., Seo, S. and Obermayer, K. (2014). A classifier-based association test for imbalanced data derived from prediction theory [32]. Neural Networks (IJCNN), 2014 International Joint Conference on, 487-493.,10.1109/IJCNN.2014.6889547

Svensson, C.-M., Krusekopf, S., Lücke, J. and Figge, M. T. (2014). Automated Detection of Circulating Tumour Cells With Naive Bayesian Classifiers [33]. Cytometry Part A, 85, 501–511.

Ladenbauer, J., Augustin, M. and Obermayer, K. (2014). How Adaptation Currents Change Threshold, Gain and Variability of Neuronal Spiking [34]. Journal of Neurophysiology, 111, 939–953.

Mohr, J., Park, J.-H. and Obermayer, K. (2014). A computer vision system for rapid search inspired by surface-based attention mechanisms from human perception [35]. Neural Networks, 60, 182 - 193.

Tobia, M. J., Guo, R., Schwarze, U., Böhmer, W., Gläscher, J., Finckh, B., Marschner, A., Büchel, C., Obermayer, K. and Sommer, T. (2014). Neural Systems for Choice and Valuation with Counterfactual Learning Signals [36]. NeuroImage, 89, 57-69.

Sheikh, A.-S., Shelton, J. A. and Lücke, J. (2014). A Truncated EM Approach for Spike-and-Slab Sparse Coding [37]. Journal of Machine Learning Research, 15, 2653–2687.

Shen, Y., Huang, R., Yan, C. and Obermayer, K. (2014). Risk-Averse Reinforcement Learning for Algorithmic Trading [38]. 2014 IEEE Computational Intelligence for Financial Engineering and Economics, 391-398.,10.1109/CIFEr.2014.6924100

Shen, Y., Stannat, W. and Obermayer, K. (2014). A Unified Framework for Risk-sensitive Markov Control Processes [39]. 53rd IEEE Conference on Decision and Control, 1073-1078.,10.1109/CDC.2014.7039524

Shen, Y., Tobia, M. J., Sommer, T. and Obermayer, K. (2014). Risk-sensitive Reinforcement Learning [40]. Neural Computation, 26, 1298-1328.

Tobia, M. J., Guo, R., Schwarze, U., Böhmer, W., Gläscher, J., Finckh, B., Marschner, A., Büchel, C., Obermayer, K. and Sommer, T. (2014). Counterfactual Q-learning in a Strategic Sequential Investment Task: Serotonergic and Dopaminergic Involvement in the Medial Prefrontal Cortex and Striatum [41]. Neuroimage

Dai, Z. and Lücke, J. (2014). Autonomous Document Cleaning – A Generative Approach to Reconstruct Strongly Corrupted Scanned Texts [42]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36, 1950–1962.

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