研究実績の概要 |
Our goal was to design AI systems that continue to learn and improve throughout their lifetime. Deep-learning models, when trained this way, catastrophically forget the past and fail. This fiscal year we worked mainly on continual learning and wrote one research paper on this topic published at NeurIPS 2020 which was accepted as an oral presentation (105 out of 9454 submissions)
- Continual Deep Learning by Functional Regularisation of Memorable Past (NeurIPS 2020) P. Pan*, S. Swaroop*, A. Immer, R. Eschenhagen, R. E. Turner, M.E. Khan
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