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Dimitris N. Metaxas | Chao Chen | Pengxiang Wu | Dimitris Metaxas | Mayank Goswami | Songzhu Zheng | Aman Goswami | Pengxiang Wu | Mayank Goswami | Songzhu Zheng | Chao Chen | A. Goswami
[1] Kiyoharu Aizawa,et al. Joint Optimization Framework for Learning with Noisy Labels , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[2] Samy Bengio,et al. Understanding deep learning requires rethinking generalization , 2016, ICLR.
[3] Xingrui Yu,et al. Co-teaching: Robust training of deep neural networks with extremely noisy labels , 2018, NeurIPS.
[4] Yoshua Bengio,et al. A Closer Look at Memorization in Deep Networks , 2017, ICML.
[5] Aritra Ghosh,et al. Making risk minimization tolerant to label noise , 2014, Neurocomputing.
[6] Jeff A. Bilmes,et al. Combating Label Noise in Deep Learning Using Abstention , 2019, ICML.
[7] Koby Crammer,et al. Adaptive regularization of weight vectors , 2009, Machine Learning.
[8] Kimin Lee,et al. Using Pre-Training Can Improve Model Robustness and Uncertainty , 2019, ICML.
[9] Richard Nock,et al. Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Jacob Goldberger,et al. Training deep neural-networks using a noise adaptation layer , 2016, ICLR.
[11] Kibok Lee,et al. Robust Inference via Generative Classifiers for Handling Noisy Labels , 2019, ICML.
[12] Yanyao Shen,et al. Learning with Bad Training Data via Iterative Trimmed Loss Minimization , 2018, ICML.
[13] Nagarajan Natarajan,et al. Learning with Noisy Labels , 2013, NIPS.
[14] Zhi-Hua Zhou,et al. On the Resistance of Nearest Neighbor to Random Noisy Labels , 2016 .
[15] Mert R. Sabuncu,et al. Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels , 2018, NeurIPS.
[16] Pietro Perona,et al. Inferring Ground Truth from Subjective Labelling of Venus Images , 1994, NIPS.
[17] M. Verleysen,et al. Classification in the Presence of Label Noise: A Survey , 2014, IEEE Transactions on Neural Networks and Learning Systems.
[18] Somesh Jha,et al. Analyzing the Robustness of Nearest Neighbors to Adversarial Examples , 2017, ICML.
[19] Carla E. Brodley,et al. Identifying Mislabeled Training Data , 1999, J. Artif. Intell. Res..
[20] Rocco A. Servedio,et al. Random classification noise defeats all convex potential boosters , 2008, ICML '08.
[21] Xiaogang Wang,et al. Learning from massive noisy labeled data for image classification , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Abhinav Gupta,et al. Learning from Noisy Large-Scale Datasets with Minimal Supervision , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Kotagiri Ramamohanarao,et al. Learning with Bounded Instance- and Label-dependent Label Noise , 2017, ICML.
[24] Leonidas J. Guibas,et al. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[26] Song Wu,et al. 3 D ShapeNets : A Deep Representation for Volumetric Shape Modeling , 2015 .
[27] Mikhail Belkin,et al. Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate , 2018, NeurIPS.
[28] Shai Shalev-Shwartz,et al. Decoupling "when to update" from "how to update" , 2017, NIPS.
[29] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[30] Koby Crammer,et al. Learning via Gaussian Herding , 2010, NIPS.
[31] Li Fei-Fei,et al. MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels , 2017, ICML.
[32] A. Tsybakov,et al. Optimal aggregation of classifiers in statistical learning , 2003 .
[33] Percy Liang,et al. Certified Defenses for Data Poisoning Attacks , 2017, NIPS.
[34] Subramanian Ramanathan,et al. Learning from multiple annotators with varying expertise , 2013, Machine Learning.
[35] Jian Sun,et al. Identity Mappings in Deep Residual Networks , 2016, ECCV.
[36] Guang Cheng,et al. Rates of Convergence for Large-scale Nearest Neighbor Classification , 2019, NeurIPS.
[37] Sanjoy Dasgupta,et al. Rates of Convergence for Nearest Neighbor Classification , 2014, NIPS.
[38] Bin Yang,et al. Learning to Reweight Examples for Robust Deep Learning , 2018, ICML.
[39] Liyuan Liu,et al. On the Variance of the Adaptive Learning Rate and Beyond , 2019, ICLR.
[40] Aditya Krishna Menon,et al. Learning with Symmetric Label Noise: The Importance of Being Unhinged , 2015, NIPS.
[41] Le Song,et al. Iterative Learning with Open-set Noisy Labels , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[42] Tao Sun,et al. Consistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss , 2006, J. Mach. Learn. Res..
[43] Dumitru Erhan,et al. Training Deep Neural Networks on Noisy Labels with Bootstrapping , 2014, ICLR.
[44] Geoffrey E. Hinton,et al. Learning to Label Aerial Images from Noisy Data , 2012, ICML.
[45] C. Qi. Deep Learning on Point Sets for 3 D Classification and Segmentation , 2016 .
[46] Tieniu Tan,et al. A Light CNN for Deep Face Representation With Noisy Labels , 2015, IEEE Transactions on Information Forensics and Security.