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pro vyhledávání: '"Telea, Alexandru C"'
As the complexity of machine learning (ML) models increases and their application in different (and critical) domains grows, there is a strong demand for more interpretable and trustworthy ML. A direct, model-agnostic, way to interpret such models is
Externí odkaz:
http://arxiv.org/abs/2304.00133
Publikováno v:
In Computers & Graphics November 2024 124
Autor:
Espadoto, Mateus, Appleby, Gabriel, Suh, Ashley, Cashman, Dylan, Li, Mingwei, Scheidegger, Carlos, Anderson, Erik W, Chang, Remco, Telea, Alexandru C
Projection techniques are often used to visualize high-dimensional data, allowing users to better understand the overall structure of multi-dimensional spaces on a 2D screen. Although many such methods exist, comparably little work has been done on g
Externí odkaz:
http://arxiv.org/abs/2111.01744
Applying dimensionality reduction (DR) to large, high-dimensional data sets can be challenging when distinguishing the underlying high-dimensional data clusters in a 2D projection for exploratory analysis. We address this problem by first sharpening
Externí odkaz:
http://arxiv.org/abs/2110.00317
Training deep neural networks is challenging when large and annotated datasets are unavailable. Extensive manual annotation of data samples is time-consuming, expensive, and error-prone, notably when it needs to be done by experts. To address this is
Externí odkaz:
http://arxiv.org/abs/2109.02717
Autor:
Liu, Sinuo, Wang, Xiaokun, Ban, Xiaojuan, Xu, Yanrui, Zhou, Jing, Kosinka, Jiří, Telea, Alexandru C.
A major issue in Smoothed Particle Hydrodynamics (SPH) approaches is the numerical dissipation during the projection process, especially under coarse discretizations. High-frequency details, such as turbulence and vortices, are smoothed out, leading
Externí odkaz:
http://arxiv.org/abs/2009.14535
Publikováno v:
In Computers & Graphics November 2023 116:287-297
Publikováno v:
In Pattern Recognition September 2023 141
Dimensionality reduction methods, also known as projections, are frequently used for exploring multidimensional data in machine learning, data science, and information visualization. Among these, t-SNE and its variants have become very popular for th
Externí odkaz:
http://arxiv.org/abs/1902.07958
Autor:
Wang, Xiaokun, Xu, Yanrui, Liu, Sinuo, Ren, Bo, Kosinka, Jiří, Telea, Alexandru C., Wang, Jiamin, Song, Chongming, Chang, Jian, Li, Chenfeng, Zhang, Jian Jun, Ban, Xiaojuan
Publikováno v:
Computational Visual Media; Oct2024, Vol. 10 Issue 5, p803-858, 56p