2017 International Joint Conference on Neural Networks (IJCNN) 2017
DOI: 10.1109/ijcnn.2017.7966253
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Information and knowing when to forget it

Abstract: Abstract-In this paper we propose several novel approaches for incorporating forgetting mechanisms into sequential prediction based machine learning algorithms. The broad premise of our work, supported and motivated in part by recent findings stemming from neurology research on the development of human brains, is that knowledge acquisition and forgetting are complementary processes, and that learning can (perhaps unintuitively) benefit from the latter too. We demonstrate that if forgetting is implemented in a … Show more

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Cited by 2 publications
(1 citation statement)
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“…When m is small, the appearance parameters change substantially in each increment. This has the effect of capturing the appearance of a part in recent history (i.e., the most recent frames) and rapidly discounting past appearance information [ 40 ]. Equivalently, the appearance parameters model an incomplete representation of the part.…”
Section: Proposed Methodsmentioning
confidence: 99%
“…When m is small, the appearance parameters change substantially in each increment. This has the effect of capturing the appearance of a part in recent history (i.e., the most recent frames) and rapidly discounting past appearance information [ 40 ]. Equivalently, the appearance parameters model an incomplete representation of the part.…”
Section: Proposed Methodsmentioning
confidence: 99%