Finding neural network weights that generalize well from small datasets is
difficult. A promising approach is to learn a weight initialization such that a
small number of weight changes results in low generalization error. We show
that this form of meta-learning can be improved by letting the learning
algorithm decide which weights to change, i.e., by learning where to learn. We
find that patterned sparsity emerges from this process, with the pattern of
sparsity varying on a problem-by-problem basis. This selective sparsity results
in better generalization and less interference in a range of few-shot and
continual learning problems. Moreover, we find that sparse learning also
emerges in a more expressive model where learning rates are meta-learned. Our
results shed light on an ongoing debate on whether meta-learning can discover
adaptable features and suggest that learning by sparse gradient descent is a
powerful inductive bias for meta-learning systems.

通过使用稀疏梯度下降的元学习方法，可以改善神经网络权重初始化的泛化能力，从而实现少量权重改变导致低泛化误差，并且这种选择性稀疏性机制使得元学习在 Few-shot 和 Continual 学习等问题上更加高效。