Web: http://arxiv.org/abs/2205.00334

May 11, 2022, 1:12 a.m. | Guruprasad Raghavan, Matt Thomson

cs.LG updates on arXiv.org arxiv.org

Deep neural networks achieve human-like performance on a variety of
perceptual and decision making tasks. However, deep networks perform poorly
when confronted with changing tasks or goals, and broadly fail to match the
flexibility and robustness of human intelligence. Here, we develop a
mathematical and algorithmic framework that enables continual training of deep
neural networks on a broad range of objectives by defining path connected sets
of neural networks that achieve equivalent functional performance on a given
machine learning task …

arxiv engineering learning machine machine learning space systems

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