Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions von Schütt, K. T., Gastegger, M., Tkatchenko, A., Müller, K.-R., Maurer, R. J.

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SchNetPack: A Deep Learning Toolbox For Atomistic Systems von Schütt, K. T, Kessel, P, Gastegger, M, Nicoli, K. A, Tkatchenko, A, Müller, K.-R

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SchNet – A deep learning architecture for molecules and materials von Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., Müller, K.-R.

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How to represent crystal structures for machine learning: Towards fast prediction of electronic properties von Schütt, K. T., Glawe, H., Brockherde, F., Sanna, A., Müller, K. R., Gross, E. K. U.

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Novel multivariate methods to track frequency shifts of neural oscillations in EEG/MEG recordings von Vidaurre, C., Gurunandan, K., Idaji, M. Jamshidi, Nolte, G., Gómez, M., Villringer, A., Müller, K.-R., Nikulin, V.V.

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Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning von Klauschen, F., Müller, K.-R., Binder, A., Bockmayr, M., Hägele, M., Seegerer, P., Wienert, S., Pruneri, G., de Maria, S., Badve, S., Michiels, S., Nielsen, T.O., Adams, S., Savas, P., Symmans, F., Willis, S., Gruosso, T., Park, M., Haibe-Kains, B., Gallas, B., Thompson, A.M., Cree, I., Sotiriou, C., Solinas, C., Preusser, M., Hewitt, S.M., Rimm, D., Viale, G., Loi, S., Loibl, S., Salgado, R., Denkert, C.

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Enhancing sensorimotor BCI performance with assistive afferent activity: An online evaluation von Vidaurre, C., Ramos Murguialday, A., Haufe, S., Gómez, M., Müller, K.-R., Nikulin, V.V.

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An introduction to kernel-based learning algorithms von Muller, K.-R., Mika, S., Ratsch, G., Tsuda, K., Scholkopf, B.

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Combining Brain-Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges von Millán, J D R, Rupp, R, Müller-Putz, G R, Murray-Smith, R, Giugliemma, C, Tangermann, M, Vidaurre, C, Cincotti, F, Kübler, A, Leeb, R, Neuper, C, Müller, K-R, Mattia, D

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Canonical maximization of coherence: A novel tool for investigation of neuronal interactions between two datasets von Vidaurre, C., Nolte, G., de Vries, I.E.J., Gómez, M., Boonstra, T.W., Müller, K.-R., Villringer, A., Nikulin, V.V.

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Engineering support vector machine kernels that recognize translation initiation sites von Zien, A., Rätsch, G., Mika, S., Schölkopf, B., Lengauer, T., Müller, K.-R.

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Neuromuscular electrical stimulation induced brain patterns to decode motor imagery von Vidaurre, C, Pascual, J, Ramos-Murguialday, A, Lorenz, R, Blankertz, B, Birbaumer, N, Müller, K.-R

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Soft Margins for AdaBoost von Rätsch, G, Onoda, T, Müller, K-r

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Predicting time series with support vector machines von Müller, K. -R., Smola, A. J., Rätsch, G., Schölkopf, B., Kohlmorgen, J., Vapnik, V.

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Designing for uncertain, asymmetric control: Interaction design for brain–computer interfaces von Williamson, J., Murray-Smith, R., Blankertz, B., Krauledat, M., Müller, K.-R.

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Prevalence of lameness on pasture-based New Zealand dairy farms: An observational study von Mason, W.A., Müller, K.R., Huxley, J.N., Laven, R.A.

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Graduate Student Literature Review: A systematic review on the associations between nonsteroidal anti-inflammatory drug use at the time of diagnosis and treatment of claw horn lame... von Mason, W.A., Cuttance, E.L., Müller, K.R., Huxley, J.N., Laven, R.A.

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Optimal dyadic decision trees von Blanchard, G., Schäfer, C., Rozenholc, Y., Müller, K.-R.

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Estimating the herd and cow level prevalence of bovine digital dermatitis on New Zealand dairy farms: A Bayesian superpopulation approach von Yang, D.A., Johnson, W.O., Müller, K.R., Gates, M.C., Laven, R.A.

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Role of the novel tryptophan hydroxylase-2 gene in Tourette syndrome von Mössner, R, Müller-Vahl, K R, Döring, N, Stuhrmann, M

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