# Adaptive Variational Continual Learning via Task-Heuristic Modelling

arXiv: https://arxiv.org/abs/2408.16517 · HTML: https://mlsanity.com/paper/2408.16517/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 29 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks](https://mlsanity.com/paper/1312.6211/) (2013)
- [Variational Continual Learning](https://mlsanity.com/paper/1710.10628/) (2017)
- [Towards Continual Reinforcement Learning: A Review and Perspectives](https://mlsanity.com/paper/2012.13490/) (2020)
- [Generalized Variational Continual Learning](https://mlsanity.com/paper/2011.12328/) (2020)
- [Continual Learning by Modeling Intra-Class Variation](https://mlsanity.com/paper/2210.05398/) (2022)
- [Learn the Time to Learn: Replay Scheduling in Continual Learning](https://mlsanity.com/paper/2209.08660/) (2022)

## Abstract (arXiv)

Variational continual learning (VCL) is a turn-key learning algorithm that has state-of-the-art performance among the best continual learning models. In our work, we explore an extension of the generalized variational continual learning (GVCL) model, named AutoVCL, which combines task heuristics for informed learning and model optimization. We demonstrate that our model outperforms the standard GVCL with fixed hyperparameters, benefiting from the automatic adjustment of the hyperparameter based on the difficulty and similarity of the incoming task compared to the previous tasks.