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Voltic: Distinguishing Volatility from Stochasticity in Recurrent Memory

Hejabi, Parsa et al.
arXiv (All) · Papers
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computation-and-languagemachine-learning
Recurrent sequence models must decide how strongly to overwrite their memory at each token. Read as Bayesian filtering, this write is the gain of a Kalman update, set by uncertainty from two sources that pull it in opposite directions: volatility, how quickly the underlying associations change, and
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