Instruct: Given a paper title, retrieve paper titles that are cited by the given paper
Query: On the Resistance of Neural Nets to Label Noise
Learning from massive noisy labeled data for image classification
1 Adversarial Perturbations of Deep Neural Networks
How Robust are Deep Neural Networks?
Bit-Regularized Optimization of Neural Nets
Distribution-Specific Hardness of Learning Neural Networks
One-sided Label Noise
Improving the Robustness of Neural Networks Using K-Support Norm Based Adversarial Training
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients
Learning to Label Aerial Images from Noisy Data
Multi-task deep neural network for multi-label learning
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks
Domain-Adversarial Neural Networks
Design Challenges and Misconceptions in Neural Sequence Labeling
Learning to Optimize Neural Nets
Label Embedding Network: Learning Label Representation for Soft Training of Deep Networks
Local minima in training of neural networks
Adversarial Reprogramming of Neural Networks
Learning from labeled and unlabeled data
Strong Universal Consistency of Neural Network Classifiers
On the Complexity of Neural Network Classifiers: A Comparison Between Shallow and Deep Architectures
NeuFlow: A runtime reconfigurable dataflow processor for vision
no . 6100 a theory of crowdfunding – a mechanism design approach with demand uncertainty and moral hazard .
Throughput Maximization in Wireless Powered Communication Networks
Nanosecond Pulse Generator with Variable Pulse Duration for the Study of Pulse Induced Biological Effects
Fractional Max-Pooling