用后验修正解释模型快速适应机制，统一多种学习场景。
Knowledge Adaptation as Posterior Correction
- 将适应视为对旧后验的修正，基于贝叶斯学习规则的对偶表示。
- 自然梯度不匹配量化新旧信息干扰，提升后验准确性减少修正量。
- 适用于持续学习、联邦学习等场景，适合研究快速适应算法者。
适应是智能的至高追求，但即使最先进的AI模型也远不及幼儿的适应能力。尽管进展显著，机器如何像人类和动物一样快速适应仍不明确。本文将适应建模为旧后验的修正，并表明包括持续学习、联邦学习、遗忘学习和模型融合在内的多种现有方法均遵循此原则。在这些场景中，更准确的后验通常带来更小的修正，从而实现更快适应。后验修正源自Khan与Rue（2023）的贝叶斯学习规则对偶表示，通过自然梯度不匹配量化旧表示与新信息间的干扰。文中展示了多个实例，说明机器可通过后验修正实现快速学习。
原文摘要 · Abstract (English)
Adaptation is the holy grail of intelligence, but even the best AI models lack the adaptability of toddlers. In spite of great progress, little is known about the mechanisms by which machines can learn to adapt as fast as humans and animals. Here, we cast adaptation as `correction' of old posteriors and show that a wide-variety of existing adaptation methods follow this very principle, including those used for continual learning, federated learning, unlearning, and model merging. In all these settings, more accurate posteriors often lead to smaller corrections and can enable faster adaptation. Posterior correction is derived by using the dual representation of the Bayesian Learning Rule of Khan and Rue (2023), where the interference between the old representation and new information is quantified by using the natural-gradient mismatch. We present many examples demonstrating how machines can learn to adapt quickly by using posterior correction.
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