论文标题
通过动态编程持续学习的元学习
Meta Continual Learning via Dynamic Programming
论文作者
论文摘要
当面对以顺序观察的类似任务时，元持续学习算法试图训练模型。尽管有希望的方法论进步，但缺乏理论框架，可以分析学习挑战，例如概括和灾难性遗忘。为此，我们为元学习〜（MCL）开发了一种新的理论方法，在该方法中，我们使用动态编程来数学上对学习动态进行建模，并为MCL问题建立最佳条件。此外，使用理论框架，我们得出了一种新的基于动态的MCL方法，该方法采用了随机分子驱动的交替优化，以平衡概括和灾难性的遗忘。我们表明，在MCL基准数据集上，我们的理论基础方法比现有最新方法更好或可比性更好地达到准确性。
Meta continual learning algorithms seek to train a model when faced with similar tasks observed in a sequential manner. Despite promising methodological advancements, there is a lack of theoretical frameworks that enable analysis of learning challenges such as generalization and catastrophic forgetting. To that end, we develop a new theoretical approach for meta continual learning~(MCL) where we mathematically model the learning dynamics using dynamic programming, and we establish conditions of optimality for the MCL problem. Moreover, using the theoretical framework, we derive a new dynamic-programming-based MCL method that adopts stochastic-gradient-driven alternating optimization to balance generalization and catastrophic forgetting. We show that, on MCL benchmark data sets, our theoretically grounded method achieves accuracy better than or comparable to that of existing state-of-the-art methods.
