arXiv:1802.07954
The State of the Art in Integrating Machine Learning into Visual Analytics
원문 초록
Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning and data visualization is still under-explored. This state-of-the-art report presents a summary of the progress that has been made by highlighting and synthesizing select research advances. Further, it presents opportunities and challenges to enhance the synergy between machine learning and visual analytics for impactful future research directions.
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Explainable Debugger for Black-box Machine Learning Models
Capturing Visualization Design Rationale
VIS-Shepherd: Constructing Critic for LLM-based Data Visualization Generation
Mapping correlations and coherence: adjacency-based approach to data visualization and regularity discovery
OLAF: An Open Life Science Analysis Framework for Conversational Bioinformatics Powered by Large Language Models
Challenging the Boundaries of Reasoning: An Olympiad-Level Math Benchmark for Large Language Models
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