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of 138
pro vyhledávání: '"Crawford, Lorin A."'
Autor:
Surasinghe, Sudam, Manivannan, Swathi Nachiar, Scarpino, Samuel V., Crawford, Lorin, Ogbunugafor, C. Brandon
Mathematical modelling has served a central role in studying how infectious disease transmission manifests at the population level. These models have demonstrated the importance of population-level factors like social network heterogeneity on structu
Externí odkaz:
http://arxiv.org/abs/2409.09096
Autor:
Meng, Kun, Ji, Mattie, Wang, Jinyu, Ding, Kexin, Kirveslahti, Henry, Eloyan, Ani, Crawford, Lorin
Tools from topological data analysis have been widely used to represent binary images in many scientific applications. Methods that aim to represent grayscale images (i.e., where pixel intensities instead take on continuous values) have been relative
Externí odkaz:
http://arxiv.org/abs/2308.14249
Autor:
Adam, Hammaad, Yin, Fan, Huibin, Hu, Tenenholtz, Neil, Crawford, Lorin, Mackey, Lester, Koenecke, Allison
Randomized experiments often need to be stopped prematurely due to the treatment having an unintended harmful effect. Existing methods that determine when to stop an experiment early are typically applied to the data in aggregate and do not account f
Externí odkaz:
http://arxiv.org/abs/2306.11839
The ability to interpret machine learning models has become increasingly important as their usage in data science continues to rise. Most current interpretability methods are optimized to work on either (\textit{i}) a global scale, where the goal is
Externí odkaz:
http://arxiv.org/abs/2302.02024
Publikováno v:
Communications Biology. 1/4/2021, Vol. 4 Issue 1, p1-3. 3p.
Publikováno v:
Journal of the American Statistical Association, 2024
In this article, we establish the mathematical foundations for modeling the randomness of shapes and conducting statistical inference on shapes using the smooth Euler characteristic transform. Based on these foundations, we propose two chi-squared st
Externí odkaz:
http://arxiv.org/abs/2204.12699
Autor:
Xie, Helen1 (AUTHOR), Crawford, Lorin2,3,4 (AUTHOR) lcrawford@microsoft.com, Conard, Ashley Mae1,2,3 (AUTHOR) ashleyconard@microsoft.com
Publikováno v:
BMC Bioinformatics. 7/30/2024, Vol. 25 Issue 1, p1-14. 14p.
Publikováno v:
In Computational Statistics and Data Analysis June 2024 194
Variational Autoencoders (VAEs) have experienced recent success as data-generating models by using simple architectures that do not require significant fine-tuning of hyperparameters. However, VAEs are known to suffer from over-regularization which c
Externí odkaz:
http://arxiv.org/abs/2007.10389
Akademický článek
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