Sequential learning paradigms pose challenges for gradient-based deep
learning due to difficulties incorporating new data and retaining prior
knowledge. While Gaussian processes elegantly tackle these problems, they
struggle with scalability and handling rich inputs, such as images. To address
these issues, we introduce a technique that converts neural networks from
weight space to function space, through a dual parameterization. Our
parameterization offers: (i) a way to scale function-space methods to large
data sets via sparsification, (ii) retention of prior knowledge when access to
past data is limited, and (iii) a mechanism to incorporate new data without
retraining. Our experiments demonstrate that we can retain knowledge in
continual learning and incorporate new data efficiently. We further show its
strengths in uncertainty quantification and guiding exploration in model-based
RL. Further information and code is available on the project website.

通过将神经网络从权重空间转化为函数空间的双参数化技术，我们解决了渐进学习方法在深度学习中的挑战，包括融入新数据和保留先前知识等问题。该技术在大规模数据集上通过稀疏化实现了函数空间方法的可扩展性，同时在有限访问过去数据时保留先前知识，并且在不需重训练的情况下融合新数据。我们的实验表明，我们能够在持续学习中保留知识并高效地融入新数据。此外，我们进一步展示了该技术在不确定性量化和基于模型的强化学习中的优势。详情和代码可在项目网站上找到。