Papers I Read
Research I keep coming back to.
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Delving Deep into Label Smoothing
IEEE TIP 2021
Chang-Bin Zhang, Peng-Tao Jiang, Qibin Hou, Yunchao Wei, Qi Han, Zhen Li, Ming-Ming Cheng
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Dropout: A Simple Way to Prevent Neural Networks from Overfitting
JMLR 2014
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov
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Part-dependent Label Noise: Towards Instance-dependent Label Noise
NeurIPS 2020
Xiaobo Xia, Tongliang Liu, Bo Han, Nannan Wang, Mingming Gong, Haifeng Liu, Gang Niu, Dacheng Tao, Masashi Sugiyama
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Some Fundamental Aspects about Lipschitz Continuity of Neural Networks
arXiv 2023
Grigory Khromov, Sidak Pal Singh
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Learning to Combat Noisy Labels via Classification Margins
arXiv 2021
Jason Z. Lin, Jelena Bradic
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Temporal Graph Networks for Deep Learning on Dynamic Graphs
arXiv 2020
Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, Michael Bronstein
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Learning from Noisy Labels with Deep Neural Networks: A Survey
IEEE TNNLS 2022
Hwanjun Song, Minseok Kim, Dongmin Park, Yooju Shin, Jae-Gil Lee