190 citations · 634 across the 21 of their papers we have counts for
8 papers · 1 filter
LangLasso: Interactive Cluster Descriptions through LLM Explanation
Raphael Buchmüller, Dennis Collaris, Linhao Meng +1
Dimensionality reduction is a powerful technique for revealing structure and potential clusters in data. However, as the axes are complex, non-linear combinations of features, they…
Visual Analytics for Explainable and Trustworthy Artificial Intelligence
Angelos Chatzimparmpas
Our society increasingly depends on intelligent systems to solve complex problems, ranging from recommender systems suggesting the next movie to watch to AI models assisting in med…
Seeing Eye to AI? Applying Deep-Feature-Based Similarity Metrics to Information Visualization
Sheng Long, Angelos Chatzimparmpas, Emma Alexander +2
Judging the similarity of visualizations is crucial to various applications, such as visualization-based search and visualization recommendation systems. Recent studies show deep-f…
Characterizing Photorealism and Artifacts in Diffusion Model-Generated Images
Negar Kamali, Karyn Nakamura, Aakriti Kumar +3
Diffusion model-generated images can appear indistinguishable from authentic photographs, but these images often contain artifacts and implausibilities that reveal their AI-generat…
How to Distinguish AI-Generated Images from Authentic Photographs
Negar Kamali, Karyn Nakamura, Angelos Chatzimparmpas +2
The high level of photorealism in state-of-the-art diffusion models like Midjourney, Stable Diffusion, and Firefly makes it difficult for untrained humans to distinguish between re…
Visualization for Trust in Machine Learning Revisited: The State of the Field in 2023
Angelos Chatzimparmpas, Kostiantyn Kucher, Andreas Kerren
Visualization for explainable and trustworthy machine learning remains one of the most important and heavily researched fields within information visualization and visual analytics…
DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting Machine
Parisa Salmanian, Angelos Chatzimparmpas, Ali Can Karaca +1
Dimensionality Reduction (DR) techniques such as t-SNE and UMAP are popular for transforming complex datasets into simpler visual representations. However, while effective in uncov…
Evaluating the Utility of Conformal Prediction Sets for AI-Advised Image Labeling
Dongping Zhang, Angelos Chatzimparmpas, Negar Kamali +1
As deep neural networks are more commonly deployed in high-stakes domains, their black-box nature makes uncertainty quantification challenging. We investigate the presentation of c…