Introduces a Transformer framework for distribution regression with a new attention operator enabling lossless compression, proves stronger functional learning than CNNs/FCNs, and provides a generalization bound with applications to LLM fine-tuning and scaling.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Generalization Analysis of Transformers in Distribution Regression
Introduces a Transformer framework for distribution regression with a new attention operator enabling lossless compression, proves stronger functional learning than CNNs/FCNs, and provides a generalization bound with applications to LLM fine-tuning and scaling.