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Layer-Wise Relevance Propagation for Neural Networks with Local Renormalization Layers.

A. Binder, G. Montavon, S. Lapuschkin, K. Müller, and W. Samek. ICANN (2), volume 9887 of Lecture Notes in Computer Science, page 63-71. Springer, (2016)

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Alexander Müller

Timo Müller University of Stuttgart

Niklas Müller University of Stuttgart

Theresa Müller University of Stuttgart

Johannes Müller University of Stuttgart

Other publications of authors with the same name

Barrier Boosting.G. Rätsch, M. Warmuth, S. Mika, T. Onoda, S. Lemm, and K. Müller. COLT, page 170-179. Morgan Kaufmann, (2000)A Mathematical Programming Approach to the Kernel Fisher Algorithm.S. Mika, G. Rätsch, and K. Müller. NIPS, page 591-597. MIT Press, (2000)Estimating vector fields using sparse basis field expansions.S. Haufe, V. Nikulin, A. Ziehe, K. Müller, and G. Nolte. NIPS, page 617-624. Curran Associates, Inc., (2008)Statistical Theory of Overtraining - Is Cross-Validation Asymptotically Effective?S. ichi Amari, N. Murata, K. Müller, M. Finke, and H. Yang. NIPS, page 176-182. MIT Press, (1995)Kernel Feature Spaces and Nonlinear Blind Souce Separation.S. Harmeling, A. Ziehe, M. Kawanabe, and K. Müller. NIPS, page 761-768. MIT Press, (2001)Going Metric: Denoising Pairwise Data.V. Roth, J. Laub, J. Buhmann, and K. Müller. NIPS, page 817-824. MIT Press, (2002)Single-trial analysis and classification of ERP components - A tutorial.B. Blankertz, S. Lemm, M. Treder, S. Haufe, and K. Müller. NeuroImage, 56 (2): 814-825 (2011)A new blind source separation framework for signal analysis and artifact rejection in functional Near-Infrared Spectroscopy.A. von Lühmann, Z. Boukouvalas, K. Müller, and T. Adali. NeuroImage, (2019)The LDA beamformer: Optimal estimation of ERP source time series using linear discriminant analysis.M. Treder, A. Porbadnigk, F. Avarvand, K. Müller, and B. Blankertz. NeuroImage, (2016)Wasserstein Training of Restricted Boltzmann Machines.G. Montavon, K. Müller, and M. Cuturi. NIPS, page 3711-3719. (2016)