However, these models have been shown to be reducible to a smaller number of self-attention heads and layers. The lottery ticket hypothesis, initially proposed by researchers Jonathan Frankle and Michael Carbin at MIT, suggests that by training deep neural networks (DNNs) from “lucky” initializations, often referred to as "winning lottery tickets,” we can train networks which are 10-100x smaller with minimal losses --- or even while achieving gains --- in performance. The Lottery Ticket Hypothesis. Recently, Deep Learning had the pleasure to welcome a new powerful metaphor: The Lottery Ticket Hypothesis (LTH). August 20, 2019 • Lukas Galke • similar version cross-published in towardsdatascience.com. Their answer is the lottery ticket hypothesis: Any large network that trains successfully contains a subnetwork that is initialized such that - when trained in isolation - it can match the accuracy of the original network in at most the same number of training iterations. The winning tickets we find have won the initialization lottery: their connections have initial weights that make training particularly effective. We present an algorithm to identify winning tickets and a series of experiments that support the lottery ticket hypothesis and the importance of … Nowadays everyone - for a glimpse of a second - has to wonder what is actually meant when referring to a desktop. Abstract: The lottery ticket hypothesis proposes that over-parameterization of deep neural networks (DNNs) aids training by increasing the probability of a “lucky” sub-network initialization being present rather than by helping the optimization process (Frankle& Carbin, 2019). Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP. We consider this phenomenon from the perspective of the lottery ticket hypothesis. Metaphors are powerful tools to transfer ideas from one mind to another. Alan Kay introduced the alternative meaning of the term ‘desktop’ at Xerox PARC in 1970. Neural networks become larger and larger and use up to billions of parameters.

06/06/2019 ∙ by Haonan Yu, et al. The lottery ticket hypothesis proposes that over-parameterization of deep neural networks (DNNs) aids training by increasing the probability of a "lucky" sub-network initialization being present rather than by helping the optimization process. Much of the recent success in NLP is due to the large Transformer-based models such as BERT (Devlin et al, 2019). ∙ Facebook ∙ 0 ∙ share . introduction.

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