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20182023
most citedExplaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics

78 citations · 162 across the 16 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

cs.GR2020

Cinema Darkroom: A Deferred Rendering Framework for Large-Scale Datasets

Jonas Lukasczyk, Christoph Garth, Matthew Larsen +5

This paper presents a framework that fully leverages the advantages of a deferred rendering approach for the interactive visualization of large-scale datasets. Geometry buffers (G-…

cs.SI2020★ 1 cited

Auditing the Sensitivity of Graph-based Ranking with Visual Analytics

Tiankai Xie, Yuxin Ma, Hanghang Tong +2

Graph mining plays a pivotal role across a number of disciplines, and a variety of algorithms have been developed to answer who/what type questions. For example, what items shall w…

cs.HC2020★ 2 cited

A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes

Yuxin Ma, Arlen Fan, Jingrui He +2

Many statistical learning models hold an assumption that the training data and the future unlabeled data are drawn from the same distribution. However, this assumption is difficult…

cs.DS2020

Localized Topological Simplification of Scalar Data

Jonas Lukasczyk, Christoph Garth, Ross Maciejewski +1

This paper describes a localized algorithm for the topological simplification of scalar data, an essential pre-processing step of topological data analysis (TDA). Given a scalar fi…

cs.LG2020★ 42 cited

Diagnosing Concept Drift with Visual Analytics

Weikai Yang, Zhen Li, Mengchen Liu +4

Concept drift is a phenomenon in which the distribution of a data stream changes over time in unforeseen ways, causing prediction models built on historical data to become inaccura…