**Probabilistic Continuously Indexed Domain Adaptation** (**PCIDA**) enjoys better theoretical guarantees to match both the mean and variance of the distribution $p(u|z)$. PCIDA can be extended to match higher-order moments.

**ERNIE-GEN** is a multi-flow sequence to sequence pre-training and fine-tuning framework which bridges the discrepancy between training and inference with an infilling generation mechanism and a noise-aware generation method. To make generation closer to human writing patterns, this framework introduces a span-by-span generation flow that trains the model to predict semantically-complete spans consecutively rather than predicting word by word. Unlike existing pre-training methods, ERNIE-GEN incorporates multi-granularity target sampling to construct pre-training data, which enhances the correlation between encoder and decoder.

ERNIE-GEN

ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation

PCIDA

Continuously Indexed Domain Adaptation

Based on the understanding that the flat local minima of the empirical risk cause the model to generalize better. Adversarial Model Perturbation (AMP) improves generalization via minimizing the **AMP loss**, which is obtained from the empirical risk by applying the **worst** norm-bounded perturbation on each point in the parameter space.

Source	Continuously Indexed Domain Adaptation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com