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When Does Non-Negative Matrix Factorization Give a Correct Decomposition into Parts?
TLDR
Theoretical results are shown to be predictive of the performance of published NMF code, by running the published algorithms on one of the synthetic image articulation databases.
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Multiscale Representations for Manifold-Valued Data
TLDR
Multiscale representations for data observed on equispaced grids and taking values in manifolds such as the sphere, the special orthogonal group, the positive definite matrices, and the Grassmann manifolds, using theExp and Log maps of those manifolds are described.
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Enhancing reproducibility for computational methods
TLDR
A novel set of Reproducibility Enhancement Principles (REP) targeting disclosure challenges involving computation is presented, which build upon more general proposals from the Transparency and Openness Promotion guidelines and emerged from workshop discussions among funding agencies, publishers and journal editors, industry participants, and researchers representing a broad range of domains.
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Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research
TLDR
A formalized set of best practice recommendations for computational scientists wishing to disseminate reproducible research, facilitate innovation by enabling data and code re-use, and enable broader communication of the output of digital scientific research are presented.
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The Scientific Method in Practice: Reproducibility in the Computational Sciences
TLDR
It is found that code, data, and ideas are each regarded differently in terms of how they are revealed and that guidance from scientific norms varies with pervasiveness of computation in the field.
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Computing Environments for Reproducibility: Capturing the "Whole Tale"
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Reproducible Research in Computational Harmonic Analysis
TLDR
The authors review their approach to reproducible computational research and how it has evolved over time, discussing the arguments for and against working reproducibly.
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Breakdown Point of Model Selection When the Number of Variables Exceeds the Number of Observations
  • D. Donoho, V. Stodden
  • Mathematics
    The IEEE International Joint Conference on…
  • 30 October 2006
TLDR
This work points out that when p > n, there is a breakdown point for standard model selection schemes, such that model selection only works well below a certain critical complexity level depending on n/p, and applies this notion to some model selection algorithms (Forward Stepwise, LASSO, LARS) in the case where pGtn.
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An empirical analysis of journal policy effectiveness for computational reproducibility
TLDR
This work evaluates the effectiveness of journal policy that requires the data and code necessary for reproducibility be made available postpublication by the authors upon request and finds it to be an improvement over no policy, but currently insufficient for reproducecibility.
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Prepublication data sharing
TLDR
Rapid release of prepublication data has served the field of genomics well and should be extended to other biological data sets, say attendees at a workshop in Toronto.
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