Paper Info
Title
On Convergence and Generalization of Dropout Training
Abstract
We study dropout in two-layer neural networks with rectified linear unit (ReLU) activations. Under mild overparametrization and assuming that the limiting kernel can separate the data distribution with a positive margin, we show that dropout training with logistic loss achieves $\epsilon$-suboptimality in test error in $O(1/\epsilon)$ iterations.
Year
Venue
DocType
2020
NIPS 2020
Conference
Volume
ISSN
Citations 
33
In Proceedings of Advances in Neural Information Processing Systems (NeurIPS), 2020
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Poorya Mianjy1184.40
R. Arora248935.97