Titlebook: Recent Advances in Big Data and Deep Learning; Proceedings of the I Luca Oneto,Nicolò Navarin,Davide Anguita Conference proceedings 2020 Sp

Engaged

muffler

東西

Perturbed Proximal Descent to Escape Saddle Points for Non-convex and Non-smooth Objective FunctionWe consider the problem of finding local minimizers in non-convex and non-smooth optimization. Under the assumption of strict saddle points, positive results have been derived for first-order methods. We present the first known results for the non-smooth case, which requires different analysis and a different algorithm.

nocturia

Luca Oneto,Nicolò Navarin,Davide AnguitaOffers recent research in Big Data and Deep Learning.Presents contributions from researchers and professionals in Big Data, Deep Learning and related areas.Includes Proceedings of the INNS Big Data an

mosque

Proceedings of the International Neural Networks Societyhttp://image.papertrans.cn/r/image/822617.jpg

BROW

https://doi.org/10.1007/978-3-030-16841-4Big Data; Deep Learning; Neural Networks; INNS Big Data and Deep Learning 2019; INNSBDDL2019

后退

978-3-030-16840-7Springer Nature Switzerland AG 2020

漸強(qiáng)

anachronistic

Dropout for Recurrent Neural Networks,opout algorithms have not been tested against one another and the naive algorithm under identical experimental conditions. This paper compares all of these algorithms and finds that the naive approach performed as well as or better than the specialised Dropout algorithms.

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