Sequential Bayesian inference over predictive functions is a natural framework for continual learning from streams of data. However, applying it to neural networks has proved challenging in practice. Addressing the drawbacks of existing techniques, we propose an optimization objective derived by formulating continual learning as sequential function-space variational inference. In contrast to existing methods that regularize neural network parameters directly, this objective allows parameters to vary widely during training, enabling better adaptation to new tasks. Compared to objectives that directly regularize neural network predictions, the proposed objective allows for more flexible variational distributions and more effective regularization. We demonstrate that, across a range of task sequences, neural networks trained via sequential function-space variational inference achieve better predictive accuracy than networks trained with related methods while depending less on maintaining a set of representative points from previous tasks.

翻译：序贯预测函数贝叶斯推断是从数据流中进行持续学习的自然框架。然而，将其应用于神经网络在实践中仍面临挑战。针对现有技术的不足，我们提出了一种通过将持续学习形式化为序贯函数空间变分推断而导出的优化目标。与直接正则化神经网络参数的现有方法不同，该目标允许参数在训练过程中广泛变化，从而更好地适应新任务。相比直接正则化神经网络预测的目标，所提目标支持更灵活的变分分布和更高效的正则化。我们证明，在多种任务序列中，通过序贯函数空间变分推断训练的神经网络在预测精度上优于采用相关方法训练的模型，同时降低了对维护先前任务代表性样本点的依赖。