Function-space Parameterization of Neural Networks for Sequential Learning

1 code implementation • 16 Mar 2024 • Aidan Scannell, Riccardo Mereu, Paul Chang, Ella Tamir, Joni Pajarinen, Arno Solin

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.

Continual Learning Gaussian Processes +1

Sparse Function-space Representation of Neural Networks

2 code implementations • 5 Sep 2023 • Aidan Scannell, Riccardo Mereu, Paul Chang, Ella Tamir, Joni Pajarinen, Arno Solin

Deep neural networks (NNs) are known to lack uncertainty estimates and struggle to incorporate new data.