All Publications

Jain, B. and Obermayer, K. (2011). Graph Quantization. J. Comput. Vision Image Understanding, 115, 946–961.

Jain, B. and Obermayer, K. (2009). Structure Spaces. Journal of Machine Learning Research, 10, 2667 – 2714.

Jain, B. and Obermayer, K. (2008). On the Sample Mean of Graphs. 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence). IEEE, 993 – 1000.,10.1109/IJCNN.2008.4633920

Jain, B. and Obermayer, K. (2011). Extending Bron Kerbosch for Solving the Maximum Weight Clique Problem. ,

Jain, B. and Obermayer, K. (2010). Accelerating Competetive Learning Graph Quantization. ,

Jain, B. and Obermayer, K. (2009). A Necessary and Sufficient Condition for Graph Matching Being Equivalent ot the Maximum Weight Clique Problem. ,

Kanev, J., Wenning, G. and Obermayer, K. (2004). Approximating the Response-Stimulus Correlation for the Integrate and Fire Neuron. Journal of Neurocomputing, 58-60, 47 – 52.

Kanev, J., Koutsou, A., Christodoulou, C. and Obermayer, K. (2016). Integrator or Coincidence Detector - a Novel Measure Based on the Discrete Reverse Correlation to Determine a Neuron's Operational Mode. Neural Computation, 28, 1-38.

Kim, H., Obermayer, K., Bode, M. and Ruswisch, D. (2001). A 1.2KBPS Speech Codec Using Spectral Vector Quantization of Differential Feature Vectors. Proceedings ICSP, 304.,

Kim, H., Obermayer, K., Bode, M. and Ruwisch, D. (2001). Efficient Speech Enhancement by Diffusive Gain Factors (DGF). Proc. Eurospeech 2001, 1867 – 1870.,

Kim, H., Obermayer, K., Bode, M. and Ruwisch, D. (2001). Real-Time Noise Suppression Based on Diffusive Gain Factors. Proceedings ICSP,

Kim, H., Obermayer, K., Bode, M. and Ruwisch, D. (2000). Real-time Noise Cancelling Based on Spectral Minimum Detection and Diffusive Gain Factors,. Proceedings of the 8th Australian International Conference on Speech Science & Technology, 256 – 261.,

Kim, H., Obermayer, K., Bode, M. and Ruwisch, D. (2000). A 1.6 KBPS Speech Codec Using Spectral Vector Quantization of Differential Features. Proceedings of the 8th Australian International Conference on Speech Science & Technology, 404 – 409.,

Kim, H., Obermayer, K. and Ruwisch, D. (2000). Real-Time Noise Cancelling with Spectral Diffusion. Proceedings of the 1st IEEE Electro/Information Technology Conference, 206 – EIT 536.,

Kim, H., Obermayer, K., Bode, M. and Ruwisch, D. (2001). Modified Spectral Substraction using Diffusive Gain Factors. Proceedings IEEE/EURASIP International Workshop on Acoustic Echo and Noise Control,

Klose, C., Seo, S. and Obermayer, K. (2005). A New Clustering Approach for Partioning Directional Data. Int. J. Rock Mech. Mining Sci., 42, 315 – 321.

Knebel, T., Hochreiter, S. and Obermayer, K. (2008). An SMO algorithm for the Potential Support Vector Machine. Neural Comput., 20, 271 – 287.

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

Koren, V., Andrei, A., Hu, M., Dragoi, V. and Obermayer, K. (2019). Reading-out task variables as a low-dimensional reconstruction of parallel spike trains in single trials. PLoS ONE, 14(10), 24.

Koren, V., Andrei, A., Hu, M., Dragol, V. and Obermayer, K. (2020). Pair-wise Synchrony and Correlations Depend on the Structure of the Population Code in Visual Cortex. Cell Reports, 2020