SAGE uses simplex-anchored graph-state equipartition and equiangular tight frames to perform structural inference on representations, bypassing distribution estimation in universal semi-supervised learning and achieving 8.52% average accuracy gains on benchmarks.
Annual Meeting of the Association for Computational Linguistics (ACL) , pages =
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Beyond Distribution Estimation: Simplex Anchored Structural Inference Towards Universal Semi-Supervised Learning
SAGE uses simplex-anchored graph-state equipartition and equiangular tight frames to perform structural inference on representations, bypassing distribution estimation in universal semi-supervised learning and achieving 8.52% average accuracy gains on benchmarks.