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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.16517 |
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| _version_ | 1866909300408975360 |
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| author | Yang, Fan |
| author_facet | Yang, Fan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16517 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adaptive Variational Continual Learning via Task-Heuristic Modelling Yang, Fan Machine Learning Artificial Intelligence 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. |
| title | Adaptive Variational Continual Learning via Task-Heuristic Modelling |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2408.16517 |