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de DJ Im2016Cité 24 foisThe success of deep neural networks hinges on our ability to accurately and efficiently optimize high-dimensional, non-convex functions. In this paper, we ...
de DJ Im2016Cité 36 foisAbstract: The training of deep neural networks is a high-dimension optimization problem with respect to the loss function of a model.
de DJ Im2016Cité 36 foisThe training of deep neural networks is a high-dimension optimization problem with respect to the loss function of a model. Unfortunately, these functions ...

PDF | The training of deep neural networks is a high-dimension optimization problem with respect to the loss function of a model. Unfortunately, these.
13 déc. 2016This paper empirically investigates the loss functions of state-of-the-art networks, and how commonly-used stochastic gradient descent ...
5 nov. 2016This paper empirically investigate the geometry of the loss functions for state-of-the-art networks with multiple stochastic optimization ...

23 avr. 2021Here is a very relevant paper: An empirical analysis of the optimization of deep network loss surfaces https://arxiv.org/pdf/1612.04010.pdf …
... Training for Deep Learning: Generalization Gap and Sharp Minima; 2017 arXiv An empirical analysis of the optimization of deep network loss surfaces ...
Oscar Castillo, Patricia Melin, Janusz Kacprzyk2020Technology & Engineering
Im, D.J., Tao, M., Branson, K.: An empirical analysis of the optimization of deep network loss surfaces (2016) 3. Goodfellow, I.J., Vinyals, O., Saxe, ...
Marcin Woźniak2021Technology & Engineering
Di, H.; Ke, X.; Peng, Z.; Dongdong, Z. Surface defect classification of ... K. An empirical analysis of the optimization of deep network loss surfaces.

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