ESCHER is a new GPU data structure and triad-count update framework that delivers speedups of up to 473.7x over prior methods on dynamic hypergraph tasks.
Jensen, and Lars Kulik
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
PIE creates predicate-aware embeddings by weighting subjectless triples and DRSD distills LLM reasoning into an SLM while decoupling confidence from rationales to improve entity alignment and enable human-in-the-loop verification.
An experimental evaluation of learned spatial indexes derives a decision tree for index selection under varying data skew, query selectivity, and storage conditions, validated on real point sets.
citing papers explorer
-
ESCHER: Efficient and Scalable Hypergraph Evolution Representation with Application to Triad Counting
ESCHER is a new GPU data structure and triad-count update framework that delivers speedups of up to 473.7x over prior methods on dynamic hypergraph tasks.
-
Predicate Importance Estimation and Decoupled Rationale-Score Distillation for Entity Alignment
PIE creates predicate-aware embeddings by weighting subjectless triples and DRSD distills LLM reasoning into an SLM while decoupling confidence from rationales to improve entity alignment and enable human-in-the-loop verification.
-
Evaluating Learned Spatial Indexes
An experimental evaluation of learned spatial indexes derives a decision tree for index selection under varying data skew, query selectivity, and storage conditions, validated on real point sets.