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Found 5 papers, 2 papers with code
On the Statistical and Information Theoretical Characteristics of DNN Representations
It has been common to argue or imply that a regularizer can be used to alter a statistical property of a hidden layer's representation and thus improve generalization or performance of deep networks.
DEEP-BO for Hyperparameter Optimization of Deep Networks
The performance of deep neural networks (DNN) is very sensitive to the particular choice of hyper-parameters.
Statistical Characteristics of Deep Representations: An Empirical Investigation
In this study, the effects of eight representation regularization methods are investigated, including two newly developed rank regularizers (RR).
Utilizing Class Information for Deep Network Representation Shaping
Motivated by the idea, we design two class-wise regularizers that explicitly utilize class information: class-wise Covariance Regularizer (cw-CR) and class-wise Variance Regularizer (cw-VR).
DNN Representations as Codewords: Manipulating Statistical Properties via Penalty Regularization
Performance of Deep Neural Network (DNN) heavily depends on the characteristics of hidden layer representations.
