The paper discusses the role of Bayesian Deep Learning (BDL) in the current deep learning research landscape. It points out that while most research focuses on predictive accuracy in supervised…
Continue readingThe paper presents an in-depth analysis of the joint effect of task similarity and overparameterization on catastrophic forgetting in continual learning. The researchers focus on two-task continual linear regression, where…
Continue readingThe paper introduces ‘Adapt & Align’, a method for continual learning of neural networks using generative models. The method addresses the issue of performance loss in neural networks when retrained…
Continue readingThe study proposes a new method, Imprecise Bayesian Continual Learning (IBCL), to address task trade-offs in continual learning. Unlike existing algorithms, IBCL does not require additional training overhead to generate…
Continue readingThe research paper ‘Elephant Neural Networks: Born to be a Continual Learner’ by Qingfeng Lan and A. Rupam Mahmood, focuses on the issue of catastrophic forgetting in continual learning. The…
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