Understanding deep learning requires rethinking …
of a model, but the absence of all regularization does not necessarily imply poor generalization er-ror. As reported by Krizhevsky et al. (2012), `2-regularization (weight decay) sometimes even helps optimization, illustrating its poorly understood nature in deep learning. Finite sample expressivity.
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arXiv:0706.3639v1 [cs.AI] 25 Jun 2007
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Deep Residual Learning for Image Recognition - …
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arXiv:1301.3781v3 [cs.CL] 7 Sep 2013
arxiv.orgFor all the following models, the training complexity is proportional to O = E T Q; (1) where E is number of the training epochs, T is the number of …
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@google.com arXiv:1609.03499v2 [cs.SD] 19 Sep 2016
arxiv.orgwhere 1 <x t <1 and = 255. This non-linear quantization produces a significantly better reconstruction than a simple linear quantization scheme. …
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