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
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
years
2026 5verdicts
UNVERDICTED 5representative 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.
Introduces BMC, a manifold bandit framework that organizes problems into a hierarchical task tree and applies Bayesian learning to balance productivity, diversity, and utility in LLM curriculum sampling.
citing papers explorer
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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.
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MEDAL: Manifold Embedding Distillation via Autoencoder Learning
MEDAL distills manifold embeddings into autoencoders to enable out-of-sample extension and held-out validation of dimension reduction methods.
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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.
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Class Angular Distortion Index for Dimensionality Reduction
CADI quantifies the preservation of relative cluster angles in low-dimensional projections using internal angles from point triples.
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Manifold Bandits: Bayesian Curriculum Learning over the Latent Geometry of Large Language Models
Introduces BMC, a manifold bandit framework that organizes problems into a hierarchical task tree and applies Bayesian learning to balance productivity, diversity, and utility in LLM curriculum sampling.