Variational Continual Learning

Cuong V. Nguyen , Yingzhen Li , Thang D. Bui , Richard E. Turner

Authors on Pith no claims yet

classification 📊 stat.ML cs.LG

keywords continuallearningtasksvariationaldeepframeworkmodelsadvances

read the original abstract

This paper develops variational continual learning (VCL), a simple but general framework for continual learning that fuses online variational inference (VI) and recent advances in Monte Carlo VI for neural networks. The framework can successfully train both deep discriminative models and deep generative models in complex continual learning settings where existing tasks evolve over time and entirely new tasks emerge. Experimental results show that VCL outperforms state-of-the-art continual learning methods on a variety of tasks, avoiding catastrophic forgetting in a fully automatic way.

This paper has not been read by Pith yet.

discussion (0)

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Preconditioned DeltaNet: Curvature-aware Sequence Modeling for Linear Recurrences
cs.LG 2026-04 unverdicted novelty 7.0

Preconditioned delta-rule models with a diagonal curvature approximation improve upon standard DeltaNet, GDN, and KDA by better approximating the test-time regression objective.
Online Bayesian Calibration under Gradual and Abrupt System Changes
cs.LG 2026-05 unverdicted novelty 6.0

BRPC is an online Bayesian calibration framework that decouples parameter tracking from discrepancy modeling for gradual nonstationarity and adds restart mechanisms to handle abrupt regime shifts.