Learning Neural Networks with Adaptive Regularization

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), pp. 11389-11400, 2019.

Cited by: 5|Views58
EI

Abstract:

Feed-forward neural networks can be understood as a combination of an intermediate representation and a linear hypothesis. While most previous works aim to diversify the representations, we explore the complementary direction by performing an adaptive and data-dependent regularization motivated by the empirical Bayes method. Specifically,...More

Code:

Data:

Your rating :
0

 

Tags
Comments