Poster
in
Workshop: Duality Principles for Modern Machine Learning
Sparse Function-space Representation of Neural Networks
Aidan Scannell · Riccardo Mereu · Paul Chang · Ella Tamir · Joni Pajarinen · Arno Solin
Keywords: [ function space ] [ continual learning ] [ Gaussian process ] [ uncertainty quantification ] [ Neural Networks ]
Deep neural networks are known to lack uncertainty estimates, struggle to incorporate new data, and suffer from catastrophic forgetting. We present a method that mitigates these issues by converting neural networks from weight-space to a low-rank function-space representation, via the so-called dual parameters. In contrast to previous work, our sparse representation captures the joint distribution over the entire data set, rather than only over a subset. This offers a compact and principled way of capturing uncertainty and enables us to incorporate new data without retraining whilst retaining predictive performance. We provide proof-of-concept demonstrations with the proposed approach for quantifying uncertainty in supervised learning on UCI benchmark tasks.