Weighted InfoNCE objectives realize specific target geometries in embedding space, with SupCon producing size-dependent inter-class similarities under imbalance while Soft SupCon and certain continuous variants preserve regular simplex or unique optima.
arXiv preprint arXiv:2407.18134 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
ProMax uses dense retrieval and dual distribution reshaping on LLM-derived profiles to guide recommender models toward preferences for unseen items, substantially boosting base model performance on public datasets.
citing papers explorer
-
A Unified Geometric Framework for Weighted Contrastive Learning
Weighted InfoNCE objectives realize specific target geometries in embedding space, with SupCon producing size-dependent inter-class similarities under imbalance while Soft SupCon and certain continuous variants preserve regular simplex or unique optima.
-
ProMax: Exploring the Potential of LLM-derived Profiles with Distribution Shaping for Recommender Systems
ProMax uses dense retrieval and dual distribution reshaping on LLM-derived profiles to guide recommender models toward preferences for unseen items, substantially boosting base model performance on public datasets.