Zobrazeno 1 - 10
of 716
pro vyhledávání: '"Barnard, Amanda"'
Explanations in machine learning are critical for trust, transparency, and fairness. Yet, complex disagreements among these explanations limit the reliability and applicability of machine learning models, especially in high-stakes environments. We fo
Externí odkaz:
http://arxiv.org/abs/2411.01956
Different prediction models might perform equally well (Rashomon set) in the same task, but offer conflicting interpretations and conclusions about the data. The Rashomon effect in the context of Explainable AI (XAI) has been recognized as a critical
Externí odkaz:
http://arxiv.org/abs/2407.18482
Machine learning methods have been remarkably successful in material science, providing novel scientific insights, guiding future laboratory experiments, and accelerating materials discovery. Despite the promising performance of these models, underst
Externí odkaz:
http://arxiv.org/abs/2402.00347
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
Autor:
Liu, Tommy, Barnard, Amanda
In this paper, we introduce the idea of decomposing the residuals of regression with respect to the data instances instead of features. This allows us to determine the effects of each individual instance on the model and each other, and in doing so m
Externí odkaz:
http://arxiv.org/abs/2305.18818
Interactions among features are central to understanding the behavior of machine learning models. Recent research has made significant strides in detecting and quantifying feature interactions in single predictive models. However, we argue that the f
Externí odkaz:
http://arxiv.org/abs/2305.10181
Autor:
Li, Sichao, Barnard, Amanda
Black box models only provide results for deep learning tasks, and lack informative details about how these results were obtained. Knowing how input variables are related to outputs, in addition to why they are related, can be critical to translating
Externí odkaz:
http://arxiv.org/abs/2209.13858
Publikováno v:
In Complementary Therapies in Clinical Practice November 2024 57
Autor:
Zhuang, Zixin, Barnard, Amanda S.
Publikováno v:
In Computational Materials Science January 2025 246
Autor:
Johnson, Gideon U., Towell-Barnard, Amanda, McLean, Christopher, Robert, Glenn, Ewens, Beverley
Publikováno v:
In International Journal of Nursing Studies December 2024 160