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Modern machine learning research relies on relatively few carefully curated datasets.
Accelerating deep learning by focusing on the biggest losers, 2019
A. H. Jiang, D. L. K. Wong, G. Zhou, D. G. Andersen, J. Dean, G. R. Ganger, G. Joshi, M. Kaminksy, M. Kozuch, Z. C. Lipton, and P. Pillai · 1910
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What Do Compressed Deep Neural Networks Forget?
S. Hooker, A. Courville, G. Clark, Y. Dauphin, and A. Frome · 1911
Earlier work this paper cites.
What Do Compressed Deep Neural Networks Forget?, Nov. 2019b
S. Hooker, A. Courville, G. Clark, Y. Dauphin, and A. Frome · 1911
Earlier work this paper cites.
Selecting Typical Instances in Instance-Based Learning
J. Zhang · 1992
Earlier work this paper cites.
Scaling laws for neural language models
J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei · 2001
Earlier work this paper cites.
Dividemix: Learning with noisy labels as semi-supervised learning
J. Li, R. Socher, and S. C. Hoi · 2002
Earlier work this paper cites.
How Much Knowledge Can You Pack Into the Parameters of a Language Model?
A. Roberts, C. Raffel, and N. Shazeer · 2002
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
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Prototype selection for interpretable classification
J. Bien and R. Tibshirani · 2012
Earlier work this paper cites.
Training deep neural networks on noisy labels with bootstrapping
S. Reed, H. Lee, D. Anguelov, C. Szegedy, D. Erhan, and A. Rabinovich · 2014
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The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification
B. Kim, C. Rudin, and J. Shah · 2015
Earlier work this paper cites.
Deep Learning Face Attributes in the Wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
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Learning from massive noisy labeled data for image classification
T. Xiao, T. Xia, Y. Yang, C. Huang, and X. Wang · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Examples are not enough, learn to criticize! Criticism for Interpretability
B. Kim, R. Khanna, and O. O. Koyejo · 2016
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Critical Learning Periods in Deep Neural Networks
A. Achille, M. Rovere, and S. Soatto · 2017
Earlier work this paper cites.
A closer look at memorization in deep networks
D. Arpit, S. Jastrzebski, N. Ballas, D. Krueger, E. Bengio, M. S. Kanwal, T. Maharaj, A. Fischer, A. Courville, Y. Bengio, et al · 2017
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Coresets for Scalable Bayesian Logistic Regression, 2017
J. H. Huggins, T. Campbell, and T. Broderick · 2017
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Understanding Black-box Predictions via Influence Functions
P. W. Koh and P. Liang · 2017
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Technical report, U.S. Department of Transportation, National Highway Traffic, Tesla Crash Preliminary Evaluation Report Safety Administration
NHTSA · 2017
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Making deep neural networks robust to label noise: A loss correction approach
G. Patrini, A. Rozza, A. Krishna Menon, R. Nock, and L. Qu · 2017
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Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data
M. Veale and R. Binns · 2017
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mixup: Beyond empirical risk minimization
H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz · 2017
Earlier work this paper cites.
Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
J. Buolamwini and T. Gebru · 2018
Cited alongside, same era.
Amazon scraps secret AI recruiting tool that showed bias against women
J. Dastin · 2018
Cited alongside, same era.
3D deep learning for detecting pulmonary nodules in CT scans
R. Gruetzemacher, A. Gupta, and D. B. Paradice · 2018
Cited alongside, same era.
Fairness Without Demographics in Repeated Loss Minimization
T. Hashimoto, M. Srivastava, H. Namkoong, and P. Liang · 2018
Cited alongside, same era.
Not all samples are created equal: Deep learning with importance sampling, 2018
A. Katharopoulos and F. Fleuret · 2018
Cited alongside, same era.
Active Learning for Convolutional Neural Networks: A Core-Set Approach, 2018
An investigation of why overparameterization exacerbates spurious correlations
S. Sagawa, A. Raghunathan, P. W. Koh, and P. Liang · 2020
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Dataset cartography: Mapping and diagnosing datasets with training dynamics
S. Swayamdipta, R. Schwartz, N. Lourie, Y. Wang, H. Hajishirzi, N. A. Smith, and Y. Choi · 2020
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A Topological Filter for Learning with Label Noise, 2020
P. Wu, S. Zheng, M. Goswami, D. Metaxas, and C. Chen · 2020
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Estimating Example Difficulty Using Variance of Gradients, 2021
C. Agarwal, D. D’souza, and S. Hooker · 2021
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The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation
O. Ahia, J. Kreutzer, and S. Hooker · 2021
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O. Sener and S. Savarese · 2018
Cited alongside, same era.
Iterative learning with open-set noisy labels
Y. Wang, W. Liu, X. Ma, J. Bailey, H. Zha, L. Song, and S. Xia · 2018
Cited alongside, same era.
Automated pulmonary nodule detection in CT images using deep convolutional neural networks
H. Xie, D. Yang, N. Sun, Z. Chen, and Y. Zhang · 2018
Cited alongside, same era.
Unsupervised label noise modeling and loss correction
E. Arazo, D. Ortego, P. Albert, N. O’Connor, and K. McGuinness · 2019
Cited alongside, same era.
Deep learning predicts hip fracture using confounding patient and healthcare variables
M. Badgeley, J. Zech, L. Oakden-Rayner, B. Glicksberg, M. Liu, W. Gale, M. McConnell, B. Percha, and T. Snyder · 2019
Cited alongside, same era.
Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications
N. Carlini, Ú. Erlingsson, and N. Papernot · 2019
Cited alongside, same era.
Does learning require memorization? A short tale about a long tail
V. Feldman · 2019
Cited alongside, same era.
What We Can’t Measure, We Can’t Understand: Challenges to Demographic Data Procurement in the Pursuit of Fairness
M. Andrus, E. Spitzer, J. Brown, and A. Xiang · 2021
Later among the works it cites.
Deep Learning Through the Lens of Example Difficulty
R. J. N. Baldock, H. Maennel, and B. Neyshabur · 2021
Later among the works it cites.
A tale of two long tails, 2021
D. D’souza, Z. Nussbaum, C. Agarwal, and S. Hooker · 2021
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When does loss-based prioritization fail?, 2021
N. T. Hu, X. Hu, R. Liu, S. Hooker, and J. Yosinski · 2021
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Just train twice: Improving group robustness without training group information
E. Z. Liu, B. Haghgoo, A. S. Chen, A. Raghunathan, P. W. Koh, S. Sagawa, P. Liang, and C. Finn · 2021
Later among the works it cites.
Deep Learning on a Data Diet: Finding Important Examples Early in Training, 2021
M. Paul, S. Ganguli, and G. K. Dziugaite · 2021
Later among the works it cites.
Core-set Sampling for Efficient Neural Architecture Search
J. Shim, K. Kong, and S.-J. Kang · 2021
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Beyond fair pay: Ethical implications of nlp crowdsourcing
B. Shmueli, J. Fell, S. Ray, and L.-W. Ku · 2021
Later among the works it cites.
The psychological well-being of content moderators: The emotional labor of commercial moderation and avenues for improving support
M. Steiger, T. J. Bharucha, S. Venkatagiri, M. J. Riedl, and M. Lease · 2021
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A. Słowik and L. Bottou · 2021
Later among the works it cites.
Training dynamic based data filtering may not work for NLP datasets
A. Talukdar, M. Dagar, P. Gupta, and V. Menon · 2021
Later among the works it cites.
Quantifying Memorization Across Neural Language Models, 2022
N. Carlini, D. Ippolito, M. Jagielski, K. Lee, F. Tramer, and C. Zhang · 2022
Closest in time.
Evaluating Distributional Distortion in Neural Language Modeling
B. LeBrun, A. Sordoni, and T. J. O’Donnell · 2022
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Prioritized training on points that are learnable, worth learning, and not yet learnt
S. Mindermann, J. Brauner, M. Razzak, M. Sharma, A. Kirsch, W. Xu, B. Höltgen, A. N. Gomez, A. Morisot, S. Farquhar, et al · 2022
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Spread spurious attribute: Improving worst-group accuracy with spurious attribute estimation
J. Nam, J. Kim, J. Lee, and J. Shin · 2022
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ResNet v1.5 for pytorch: Nvidia NGC, 2022
NVIDIA · 2022
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When does dough become a bagel? Analyzing the remaining mistakes on ImageNet, 2022
V. Vasudevan, B. Caine, R. Gontijo-Lopes, S. Fridovich-Keil, and R. Roelofs · 2022
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Emergent abilities of large language models, 2022
J. Wei, Y. Tay, R. Bommasani, C. Raffel, B. Zoph, S. Borgeaud, D. Yogatama, M. Bosma, D. Zhou, D. Metzler, E. H. Chi, T. Hashimoto, O. Vinyals, P. Liang, J. Dean, and W. Fedus · 2022
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Correct-n-contrast: A contrastive approach for improving robustness to spurious correlations
M. Zhang, N. S. Sohoni, H. R. Zhang, C. Finn, and C. Ré · 2022
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