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Found 4 papers, 2 papers with code
Extracting Human Attention through Crowdsourced Patch Labeling
We demonstrated the effectiveness of our method in mitigating bias through improved classification accuracy and the refined focus of the model.
ZADU: A Python Library for Evaluating the Reliability of Dimensionality Reduction Embeddings
To address this issue, we present ZADU, a Python library that provides distortion measures.
Uniform Manifold Approximation with Two-phase Optimization
We introduce Uniform Manifold Approximation with Two-phase Optimization (UMATO), a dimensionality reduction (DR) technique that improves UMAP to capture the global structure of high-dimensional data more accurately.
Distortion-Aware Brushing for Interactive Cluster Analysis in Multidimensional Projections
Brushing is an everyday interaction in 2D scatterplots, which allows users to select and filter data points within a continuous, enclosed region and conduct further analysis on the points.
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