Explicit dropout reformulates stochastic dropout as deterministic loss penalties for Transformers, matching or exceeding standard performance with independent control per component.
Wangchunshu Zhou, Tao Ge, Ke Xu, Furu Wei, and Ming Zhou
2 Pith papers cite this work, alongside 34 external citations. Polarity classification is still indexing.
2
Pith papers citing it
34
external citations · external index
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Language models detect, localize, and distinguish dropout from Gaussian noise applied to their activations, often with high accuracy.
citing papers explorer
-
Explicit Dropout: Deterministic Regularization for Transformer Architectures
Explicit dropout reformulates stochastic dropout as deterministic loss penalties for Transformers, matching or exceeding standard performance with independent control per component.
-
Language models recognize dropout and Gaussian noise applied to their activations
Language models detect, localize, and distinguish dropout from Gaussian noise applied to their activations, often with high accuracy.