Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper.
Query: Spectral Norm Regularization for Improving the Generalizability of Deep Learning
A Simple Weight Decay Can Improve Generalization
Lipschitz Properties for Deep Convolutional Networks
Efficient L1 Regularized Logistic Regression
In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning
on the regularization of wgans .
Compression of Deep Convolutional Neural Networks under Joint Sparsity Constraints
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks
Towards Robust Training of Neural Networks by Regularizing Adversarial Gradients
Spectral Inference Networks: Unifying Spectral Methods With Deep Learning.
Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity
Adding Gradient Noise Improves Learning for Very Deep Networks
Maximum Principle Based Algorithms for Deep Learning
Adversarial Noise Layer: Regularize Neural Network by Adding Noise
Imposing Hard Constraints on Deep Networks: Promises and Limitations
Normalization Propagation: A Parametric Technique for Removing Internal Covariate Shift in Deep Networks
Attention-Based Guided Structured Sparsity of Deep Neural Networks
Training Skinny Deep Neural Networks with Iterative Hard Thresholding Methods
l0 regularization .
Adaptive Regularization With the Structure Tensor
Twin Networks: Using the Future as a Regularizer
Real-time simulation as a way to improve daily operations in an emergency room
Towards the utilization of EEG as a brain imaging tool
Grab 'n Run: Secure and Practical Dynamic Code Loading for Android Applications
implementation research : a synthesis of the literature .
Consistent inference of probabilities in layered networks: predictions and generalizations