Scientists Re-Evaluate Continual Learning With Few-Shot Adaptation
New research explores continual learning methods designed to enhance the stability and plasticity of machine learning models. The study, titled 'Re-Evaluating Continual Learning with Few-Shot Adaptation,' was authored by Amogh Inamdar, Matthew So, Vici Milenia, and Richard Zemel.
Topics
Developing
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Sources · 7 independent
Modernity/arxiv
“Re-Evaluating Continual Learning with Few-Shot Adaptation. Authors: Amogh Inamdar, Matthew So, Vici Milenia, Richard Zemel Abstract: Continual learning methods aim to maximize the stability and plasticity of machine learning models that are trained on a sequ...”
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