Zobrazeno 1 - 10
of 6 758
pro vyhledávání: '"Wood, Andrew"'
Autor:
Seshadri, Rikhil, Siva, Jayant, Bartholomew, Angelica, Goebel, Clara, Wallerstein-King, Gabriel, Morato, Beatriz López, Heller, Nicholas, Scovell, Jason, Campbell, Rebecca, Wood, Andrew, Ozery-Flato, Michal, Barros, Vesna, Gabrani, Maria, Rosen-Zvi, Michal, Tejpaul, Resha, Ramesh, Vidhyalakshmi, Papanikolopoulos, Nikolaos, Regmi, Subodh, Ward, Ryan, Abouassaly, Robert, Campbell, Steven C., Remer, Erick, Weight, Christopher
Kidney cancer is a global health concern, and accurate assessment of patient frailty is crucial for optimizing surgical outcomes. This paper introduces AI Age Discrepancy, a novel metric derived from machine learning analysis of preoperative abdomina
Externí odkaz:
http://arxiv.org/abs/2407.00438
Autor:
Hershcovitch, Moshik, Choshen, Leshem, Wood, Andrew, Enmouri, Ilias, Chin, Peter, Sundararaman, Swaminathan, Harnik, Danny
With the growth of model sizes and scale of their deployment, their sheer size burdens the infrastructure requiring more network and more storage to accommodate these. While there is a vast literature about reducing model sizes, we investigate a more
Externí odkaz:
http://arxiv.org/abs/2404.15198
Publikováno v:
Adv. Theory Simul. 2024, 2301227
The fractal dimension of a surface allows its degree of roughness to be characterized quantitatively. However, limited effort is attempted to calculate the fractal dimension of surfaces computed from precisely known atomic coordinates from computatio
Externí odkaz:
http://arxiv.org/abs/2401.11737
Functional data analysis offers a diverse toolkit of statistical methods tailored for analyzing samples of real-valued random functions. Recently, samples of time-varying random objects, such as time-varying networks, have been increasingly encounter
Externí odkaz:
http://arxiv.org/abs/2312.07741
Autor:
Wood, Andrew
Machine Learning (ML) is expensive: it requires machines that possess large compute capabilities, high memory and storage capacities, and exceedingly large amounts of time for models to train. When training a model, ML programs in general follow a st
Externí odkaz:
https://hdl.handle.net/2144/48020
The paper considers the distribution of a general linear combination of central and non-central chi-square random variables by exploring the branch cut regions that appear in the standard Laplace inversion process. Due to the original interest from t
Externí odkaz:
http://arxiv.org/abs/2305.07434