Jan. 1, 2023, midnight | Sanket Vaibhav Mehta, Darshan Patil, Sarath Chandar, Emma Strubell

JMLR www.jmlr.org

The lifelong learning paradigm in machine learning is an attractive alternative to the more prominent isolated learning scheme not only due to its resemblance to biological learning but also its potential to reduce energy waste by obviating excessive model re-training. A key challenge to this paradigm is the phenomenon of catastrophic forgetting. With the increasing popularity and success of pre-trained models in machine learning, we pose the question: What role does pre-training play in lifelong learning, specifically with respect to …

challenge energy investigation lifelong learning machine machine learning paradigm pre-training reduce role training waste

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