Instruct: Given the title of a scientific paper, retrieve paper titles that are cited by the given paper
Query: HOW DO NEURAL NETWORKS OVERCOME LABEL NOISE?
Learning from Noisy Labels with Deep Neural Networks.
Classification in the Presence of Label Noise: A Survey
Support Vector Machines Under Adversarial Label Noise
Learning From Noisy Singly-labeled Data
Learning from massive noisy labeled data for image classification
Neural Simpletrons: Learning in the Limit of Few Labels with Directed Generative Networks
Towards instance-dependent label noise-tolerant classification: a probabilistic approach
Lessons in Neural Network Training: Overfitting May be Harder than Expected
On the Computational Efficiency of Training Neural Networks
A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels
Classification with Asymmetric Label Noise: Consistency and Maximal Denoising
Learning with Symmetric Label Noise: The Importance of Being Unhinged
Learning kernel logistic regression in the presence of class label noise
Learning from noisy examples
Introducing Noise in Decentralized Training of Neural Networks
On the Robustness of a Neural Network
The Neural Noisy Channel
Cost-sensitive learning with noisy labels
Local minima in training of neural networks
Neural Networks: Tricks of the Trade
Novel UWB low-profile sinuous slot antenna
Computational Stereo
Scene-centric Joint Parsing of Cross-view Videos
Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels
Cyber Security and the Internet of Things: Vulnerabilities, Threats, Intruders and Attacks