CAPE produces spatially grounded natural-language explanations for document layouts using pattern detection and multi-level context, rated more helpful than content-only baselines in a user study.
Stop misusing t- SNE and UMAP for visual analytics
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
MEDAL distills manifold embeddings into autoencoders to enable out-of-sample extension and held-out validation of dimension reduction methods.
LLM-augmented semantic steering lets analysts reshape text embedding projections by providing semantic groupings that an LLM externalizes and extends to improve alignment with intended structures using minimal interaction.
CADI quantifies the preservation of relative cluster angles in low-dimensional projections using internal angles from point triples.
citing papers explorer
-
Context-Aware Explanations for Spatialized Document Layouts
CAPE produces spatially grounded natural-language explanations for document layouts using pattern detection and multi-level context, rated more helpful than content-only baselines in a user study.
-
LLM-Augmented Semantic Steering of Text Embedding Projection Spaces
LLM-augmented semantic steering lets analysts reshape text embedding projections by providing semantic groupings that an LLM externalizes and extends to improve alignment with intended structures using minimal interaction.