Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Kannan, Arjun"'
Autor:
Kannan, Arjun, Yadav, Nitin
We investigate vortex dynamics in three magnetic regions, viz., Quiet Sun, Weak Plage, and Strong Plage, using realistic three-dimensional simulations from a comprehensive radiation-MHD code, MURaM. We find that the spatial extents and spatial distri
Externí odkaz:
http://arxiv.org/abs/2408.08225
Large Language Models such as GPTs (Generative Pre-trained Transformers) exhibit remarkable capabilities across a broad spectrum of applications. Nevertheless, due to their intrinsic complexity, these models present substantial challenges in interpre
Externí odkaz:
http://arxiv.org/abs/2407.11215
Deriving meaningful representations from complex, high-dimensional data in unsupervised settings is crucial across diverse machine learning applications. This paper introduces a framework for multi-scale graph network embedding based on spectral grap
Externí odkaz:
http://arxiv.org/abs/2406.02778
In recent years, many Machine Learning (ML) explanation techniques have been designed using ideas from cooperative game theory. These game-theoretic explainers suffer from high complexity, hindering their exact computation in practical settings. In o
Externí odkaz:
http://arxiv.org/abs/2303.10216
Due to their power and ease of use, tree-based machine learning models, such as random forests and gradient-boosted tree ensembles, have become very popular. To interpret them, local feature attributions based on marginal expectations, e.g. marginal
Externí odkaz:
http://arxiv.org/abs/2302.08434
This article is a companion paper to our earlier work Miroshnikov et al. (2021) on fairness interpretability, which introduces bias explanations. In the current work, we propose a bias mitigation methodology based upon the construction of post-proces
Externí odkaz:
http://arxiv.org/abs/2111.11259
Publikováno v:
In Journal of Arid Environments June 2024 222
In this article, we study feature attributions of Machine Learning (ML) models originating from linear game values and coalitional values defined as operators on appropriate functional spaces. The main focus is on random games based on the conditiona
Externí odkaz:
http://arxiv.org/abs/2102.10878
Publikováno v:
Machine Learning Journal (2022), Springer
The objective of this article is to introduce a fairness interpretability framework for measuring and explaining the bias in classification and regression models at the level of a distribution. In our work, we measure the model bias across sub-popula
Externí odkaz:
http://arxiv.org/abs/2011.03156
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.