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pro vyhledávání: '"Müller, Christian L."'
We present a framework for smooth optimization of explicitly regularized objectives for (structured) sparsity. These non-smooth and possibly non-convex problems typically rely on solvers tailored to specific models and regularizers. In contrast, our
Externí odkaz:
http://arxiv.org/abs/2307.03571
Autor:
Rügamer, David, Bender, Andreas, Wiegrebe, Simon, Racek, Daniel, Bischl, Bernd, Müller, Christian L., Stachl, Clemens
Recommender Systems (RS) pervade many aspects of our everyday digital life. Proposed to work at scale, state-of-the-art RS allow the modeling of thousands of interactions and facilitate highly individualized recommendations. Conceptually, many RS can
Externí odkaz:
http://arxiv.org/abs/2205.13080
Autor:
Thalmeier, Dominik, Miller, Gregor, Schneltzer, Elida, Hurt, Anja, de Angelis, Martin Hrabě, Becker, Lore, Müller, Christian L., Maier, Holger
Publikováno v:
BMC Neurosci 23, 81 (2022)
Hearing loss is a major health problem and psychological burden in humans. Mouse models offer a possibility to elucidate genes involved in the underlying developmental and pathophysiological mechanisms of hearing impairment. To this end, large-scale
Externí odkaz:
http://arxiv.org/abs/2112.08961
Autor:
Hyun, Sangwon, Mishra, Aditya, Follett, Christopher L., Jonsson, Bror, Kulk, Gemma, Forget, Gael, Racault, Marie-Fanny, Jackson, Thomas, Dutkiewicz, Stephanie, Müller, Christian L., Bien, Jacob
Publikováno v:
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, year 2022, volume 478, number 2262, pages 20210875
Remote sensing observations from satellites and global biogeochemical models have combined to revolutionize the study of ocean biogeochemical cycling, but comparing the two data streams to each other and across time remains challenging due to the str
Externí odkaz:
http://arxiv.org/abs/2111.08736
We introduce GGLasso, a Python package for solving General Graphical Lasso problems. The Graphical Lasso scheme, introduced by (Friedman 2007) (see also (Yuan 2007; Banerjee 2008)), estimates a sparse inverse covariance matrix $\Theta$ from multivari
Externí odkaz:
http://arxiv.org/abs/2110.10521
Publikováno v:
Journal of Open Source Software, 6(65), 3634, 2021
We present `latentcor`, an R package for correlation estimation from data with mixed variable types. Mixed variables types, including continuous, binary, ordinal, zero-inflated, or truncated data are routinely collected in many areas of science. Accu
Externí odkaz:
http://arxiv.org/abs/2108.09180
We characterize and remedy a failure mode that may arise from multi-scale dynamics with scale imbalances during training of deep neural networks, such as Physics Informed Neural Networks (PINNs). PINNs are popular machine-learning templates that allo
Externí odkaz:
http://arxiv.org/abs/2107.00940
Many scientific datasets are compositional in nature. Important biological examples include species abundances in ecology, cell-type compositions derived from single-cell sequencing data, and amplicon abundance data in microbiome research. Here, we p
Externí odkaz:
http://arxiv.org/abs/2106.11234
Autor:
Rügamer, David, Kolb, Chris, Fritz, Cornelius, Pfisterer, Florian, Kopper, Philipp, Bischl, Bernd, Shen, Ruolin, Bukas, Christina, Sousa, Lisa Barros de Andrade e, Thalmeier, Dominik, Baumann, Philipp, Kook, Lucas, Klein, Nadja, Müller, Christian L.
In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of additive regression models and deep networks. Our implementation encom
Externí odkaz:
http://arxiv.org/abs/2104.02705
Autor:
Maddu, Suryanarayana, Sturm, Dominik, Cheeseman, Bevan L., Müller, Christian L., Sbalzarini, Ivo F.
Numerical methods for approximately solving partial differential equations (PDE) are at the core of scientific computing. Often, this requires high-resolution or adaptive discretization grids to capture relevant spatio-temporal features in the PDE so
Externí odkaz:
http://arxiv.org/abs/2101.06182