Christfried Webers

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On-line Machine Learning using Stochastic Gradient Descent is an inherently sequential computation. This makes it difficult to improve performance by simply employing parallel architectures. Langford et al. made a modification to the standard stochastic gradient descent approach which opens up the possibility of parallel computation. They also proved that(More)
Motion control of vehicles under uncertain, noisy, and discontinuous positioning is essential in autonomous navigation in unknown environments. This article suggests two methods for motion control, where the initial parameters of the on-line control are physically explainable, the resulting trajectory as well as the control parameters are asymptotically(More)
Modern genomics techniques generate overwhelming quantities of data. Extracting population genetic variation demands computationally efficient methods to determine genetic relatedness between individuals (or "samples") in an unbiased manner, preferably de novo. Rapid estimation of genetic relatedness directly from sequencing data has the potential to(More)
Robust tracking of vehicles under uncertain, noisy, and discontinuous positioning is a significant part of autonomous navigation in unknown environments. This article suggests two methods for track control, where the initial parameters of the on-line control are physically explainable, the resulting track as well as the control parameters are asymptotically(More)