Continual Learning via Sequential Function-Space Variational Inference

By Tim G. J. Rudner, Freddie Bickford-Smith, Qixuan Feng, Yee Whye Teh, Yarin Gal. 2023

59 citations recorded by the source corpus.

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.

Subfields: Probabilistic & Bayesian Methods, Meta & Continual Learning

Citations in the corpus

• A Unified and General Framework for Continual Learning (2024, unknown): “Representative works include (Nguyen et al., 2018; Kurle et al., 2019; Kao et al., 2021; Henning et al., 2021; Pan et al., 2020; Titsias et al., 2020; Rudner et al., 2022).”