Instruct: Given a paper title, retrieve paper titles that are cited by the given paper.
Query: Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks
Training Deep Neural Networks on Noisy Labels with Bootstrapping
Collaborative Layer-wise Discriminative Learning in Deep Neural Networks
Classification in the Presence of Label Noise: A Survey
Sequencediscriminative training of deep neural networks
Towards Deep Neural Network Architectures Robust to Adversarial Examples
An Entropic Optimal Transport Loss for Learning Deep Neural Networks under Label Noise in Remote Sensing Images
Robust Convolutional Neural Networks under Adversarial Noise
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients
Bidirectional Learning for Robust Neural Networks
A boosting approach to remove class label noise
A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Examples
Towards the first adversarially robust neural network model on MNIST
Progressive Stochastic Learning for Noisy Labels
Loss factorization, weakly supervised learning and label noise robustness
Learning from Noisy Labels with Distillation
Cost-sensitive learning with noisy labels
How Robust are Deep Neural Networks?
Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks
Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction
Hardening Stratum, the Bitcoin Pool Mining Protocol
Fusion of Electrocardiogram and Arterial Blood Pressure Signals for Authentication in Wearable Medical Systems
Approaches for Neural-Network Language Model Adaptation
Text Line Detection for Heterogeneous Documents