Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper.
Query: C LOSE-TO-CLEAN REGULARIZATION RELATES VIRTUAL ADVERSARIAL TRAINING , LADDER NETWORKS AND OTHERS
Distributional Smoothing with Virtual Adversarial Training
Adaptive dropout for training deep neural networks
MagNet: A Two-Pronged Defense against Adversarial Examples
Fixing Weight Decay Regularization in Adam
Detecting Adversarial Examples in Deep Networks with Adaptive Noise Reduction
Fast Convergence of Regularized Learning in Games
Zero Shot Learning via Multi-scale Manifold Regularization
Adaptive Regularization With the Structure Tensor
Generalized Loss-Sensitive Adversarial Learning with Manifold Margins
Online regularized learning with pairwise loss functions
Idealised Bayesian Neural Networks Cannot Have Adversarial Examples: Theoretical and Empirical Study
Foundations of Adversarial Machine Learning
Stochastic gradient adaptation under general error criteria
Partial Transfer Learning with Selective Adversarial Networks
semi - supervised learning using adversarial networks .
Adversarial Active Learning for Deep Networks: a Margin Based Approach
Self-Paced Learning: an Implicit Regularization Perspective
Adversarial Feature Learning
The Robust Manifold Defense: Adversarial Training using Generative Models
l0 regularization .
Towards real-time community detection in large networks
Label Consistent K-SVD: Learning a Discriminative Dictionary for Recognition
A rare sugar xylitol. Part II: biotechnological production and future applications of xylitol
Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition
Intelligent agent technology in e-commerce