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www.fharrell.com | ||
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www.nicholas-ollberding.com
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| | | | | This is post is to introduce members of the Cincinnati Children's Hospital Medical Center R Users Group (CCHMC-RUG) to some of the functionality provided by Frank Harrell's Hmisc and rms packages for data description and predictive modeling. | |
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www.huber.embl.de
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| | | | | If you are a biologist and want to get the best out of the powerful methods of modern computational statistics, this is your book. | |
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hbiostat.org
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| | | | | In this article I provide much more extensive simulations showing the near perfect agreement between the odds ratio (OR) from a proportional odds (PO) model, and the Wilcoxon two-sample test statistic. The agreement is studied by degree of violation of the PO assumption and by the sample size. A refinement in the conversion formula between the OR and the Wilcoxon statistic scaled to 0-1 (corcordance probability) is provided. | |
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iclr-blogposts.github.io
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| | | The product between the Hessian of a function and a vector, the Hessian-vector product (HVP), is a fundamental quantity to study the variation of a function. It is ubiquitous in traditional optimization and machine learning. However, the computation of HVPs is often considered prohibitive in the context of deep learning, driving practitioners to use proxy quantities to evaluate the loss geometry. Standard automatic differentiation theory predicts that the computational complexity of an HVP is of the same order of magnitude as the complexity of computing a gradient. The goal of this blog post is to provide a practical counterpart to this theoretical result, showing that modern automatic differentiation frameworks, JAX and PyTorch, allow for efficient computat... | ||
