Explicit Dropout: Deterministic Regularization for Transformer Architectures

ArXi:2604.20505v1 Announce Type: new Dropout is a widely used regularization technique in deep learning, but its effects are typically realized through stochastic masking rather than explicit optimization objectives. We propose a deterministic formulation that expresses dropout as an additive regularizer directly incorporated into the