Variational Learning is Effective for Large Deep Networks
Yuesong Shen Nico Daheim Bai Cong Peter Nickl Gian Maria Marconi Clement Bazan Rio Yokota Iryna Gurevych Daniel Cremers Mohammad Emtiyaz Khan Thomas Möllenhoff
We give extensive empirical evidence against the common belief that
variational learning is ineffective for large neural networks. We show that an
optimizer called Improved Variational Online Newton (IVON) consistently matches
or outperforms Adam for training large networks such as GPT-2 and ResNets from
scratch. IVON's computational costs are nearly identical to Adam but its
predictive uncertainty is better. We show several new use cases of IVON where
we improve fine-tuning and model merging in Large Language Models, accurately
predict generalization error, and faithfully estimate sensitivity to data. We
find overwhelming evidence in support of effectiveness of variational learning.