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“A Stochastic Approximation Method”
H. Robbins and S. Monro · 1951
Earlier work this paper cites.
“Python reference manual”
G. Rossum and F.. Jr · 1995
Earlier work this paper cites.
“Semi-supervised learning by entropy minimization”
Y. Grandvalet and Y. Bengio · 2004
Earlier work this paper cites.
“Matplotlib: A 2D graphics environment”
J.. Hunter · 2007
Earlier work this paper cites.
“Imagenet: A large-scale hierarchical image database”
J. Deng, W. Dong, R. Socher, L. Li, K. Li and L. Fei-Fei · 2009
Earlier work this paper cites.
“Learning multiple layers of features from tiny images”, 2009
A. Krizhevsky · 2009
Earlier work this paper cites.
“Watermarking the outputs of structured prediction with an application in statistical machine translation.”
A. Venugopal, J. Uszkoreit, D. Talbot, F.. Och and J. Ganitkevitch · 2011
Earlier work this paper cites.
“Sequence transduction with recurrent neural networks”
A. Graves · 2012
Earlier work this paper cites.
“Adam: A method for stochastic optimization”
D. Kingma and J. Ba · 2014
Earlier work this paper cites.
“Strategic classification”
M. Hardt, N. Megiddo, C. Papadimitriou and M. Wootters · 2016
Earlier work this paper cites.
“Deep Residual Learning for Image Recognition”
K. He, X. Zhang, S. Ren and J. Sun · 2016
Earlier work this paper cites.
“Deconvolving feedback loops in recommender systems”
A. Sinha, D.. Gleich and K. Ramani · 2016
Earlier work this paper cites.
“YFCC100M: The new data in multimedia research”
B. Thomee, D.. Shamma, G. Friedland, B. Elizalde, K. Ni, D. Poland, D. Borth and L. Li · 2016
Earlier work this paper cites.
“Situation recognition: Visual semantic role labeling for image understanding”
M. Yatskar, L. Zettlemoyer and A. Farhadi · 2016
Earlier work this paper cites.
“Dawnbench: An end-to-end deep learning benchmark and competition”
C. Coleman, D. Narayanan, D. Kang, T. Zhao, J. Zhang, L. Nardi, P. Bailis, K. Olukotun, C. Ré and M. Zaharia · 2017
Earlier work this paper cites.
“Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints”
J. Zhao, T. Wang, M. Yatskar, V. Ordóñez and K. Chang · 2017
Earlier work this paper cites.
“How algorithmic confounding in recommendation systems increases homogeneity and decreases utility”
A.. Chaney, B.. Stewart and B.. Engelhardt · 2018
Earlier work this paper cites.
“Cinic-10 is not imagenet or cifar-10”
L.. Darlow, E.. Crowley, A. Antoniou and A.. Storkey · 2018
Earlier work this paper cites.
“Hierarchical Neural Story Generation”
A. Fan, M. Lewis and Y. Dauphin · 2018
Earlier work this paper cites.
“Fairness without demographics in repeated loss minimization”
T.. Hashimoto, M. Srivastava, H. Namkoong and P. Liang · 2018
Earlier work this paper cites.
“Human interaction with recommendation systems”
S. Schmit and C. Riquelme · 2018
Earlier work this paper cites.
“A dirt-t Approach to Unsupervised Domain Adaptation”
R. Shu, H.. Bui, H. Narui and S. Ermon · 2018
Earlier work this paper cites.
“Calibrated recommendations”
H. Steck · 2018
Earlier work this paper cites.
“Toxic Comment Classification Challenge”, https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge/overview , 2018
Jigsaw team · 2018
Earlier work this paper cites.
“Queens are powerful too: Mitigating gender bias in dialogue generation”
E. Dinan, A. Fan, A. Williams, J. Urbanek, D. Kiela and J. Weston · 2019
Cited alongside, same era.
“OpenWebText Corpus”, http://Skylion007.github.io/OpenWebTextCorpus , 2019
A. Gokaslan and V. Cohen · 2019
Cited alongside, same era.
“Degenerate feedback loops in recommender systems”
R. Jiang, S. Chiappa, T. Lattimore, A. György and P. Kohli · 2019
Cited alongside, same era.
“Feature-wise bias amplification”
K. Leino, E. Black, M. Fredrikson, S. Sen and A. Datta · 2019
Cited alongside, same era.
“CIFAR10 Training BaiduNet9”, https://github.com/BAIDU-USA-GAIT-LEOPARD/CIFAR10-Training-BaiduNet9 , 2019
B. Li, Z. Cheng and Y. Bao · 2019
Cited alongside, same era.
“Decoupled weight decay regularization”
“Optimal regularization can mitigate double descent”
P. Nakkiran, P. Venkat, S. Kakade and T. Ma · 2020
Later among the works it cites.
“An overview of deep semi-supervised learning”
Y. Ouali, C. Hudelot and M. Tami · 2020
Later among the works it cites.
“Performative prediction”
J. Perdomo, T. Zrnic, C. Mendler-Dünner and M. Hardt · 2020
Later among the works it cites.
“Stegastamp: Invisible hyperlinks in physical photographs”
M. Tancik, B. Mildenhall and R. Ng · 2020
Later among the works it cites.
“Jigsaw Multilingual Toxic Comment Classification”, https://www.kaggle.com/c/jigsaw-multilingual-toxic-comment-classification , 2020
Jigsaw team · 2020
Later among the works it cites.
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I. Loshchilov and F. Hutter · 2019
Cited alongside, same era.
“Pytorch: An imperative style, high-performance deep learning library”
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai and S. Chintala · 2019
Cited alongside, same era.
“Language models are unsupervised multitask learners”
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei and I. Sutskever · 2019
Cited alongside, same era.
“The woman worked as a babysitter: On biases in language generation”
E. Sheng, K. Chang, P. Natarajan and N. Peng · 2019
Cited alongside, same era.
“Jigsaw Unintended Bias in Toxicity Classification”, https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification , 2019
Jigsaw team · 2019
Cited alongside, same era.
“Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations”
T. Wang, J. Zhao, M. Yatskar, K. Chang and V. Ordonez · 2019
Cited alongside, same era.
“HuggingFace’s Transformers: State-of-the-art Natural Language Processing”
T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, J. Davison, S. Shleifer, P. von Platen, C. Ma, Y. Jernite, J. Plu, C. Xu, T.. Scao, S. Gugger, M. Drame, Q. Lhoest and A.. Rush · 2019
Cited alongside, same era.
Y. Bahri, E. Dyer, J. Kaplan, J. Lee and U. Sharma · 2021
Later among the works it cites.
“BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation”
J. Dhamala, T. Sun, V. Kumar, S. Krishna, Y. Pruksachatkun, K. Chang and R. Gupta · 2021
Later among the works it cites.
“Anticipating safety issues in e2e conversational ai: Framework and tooling”
E. Dinan, G. Abercrombie, A.. Bergman, S. Spruit, D. Hovy, Y. Boureau and V. Rieser · 2021
Later among the works it cites.
“A brief review of domain adaptation”
A. Farahani, S. Voghoei, K. Rasheed and H.. Arabnia · 2021
Later among the works it cites.
“Are GAN generated images easy to detect? A critical analysis of the state-of-the-art”
D. Gragnaniello, D. Cozzolino, F. Marra, G. Poggi and L. Verdoliva · 2021
Later among the works it cites.
“How to learn when data reacts to your model: performative gradient descent”
Z. Izzo, L. Ying and J. Zou · 2021
Later among the works it cites.
“Designing toxic content classification for a diversity of perspectives”
D. Kumar, P.. Kelley, S. Consolvo, J. Mason, E. Bursztein, Z. Durumeric, K. Thomas and M. Bailey · 2021
Later among the works it cites.
“Outside the echo chamber: Optimizing the performative risk”
J.. Miller, J.. Perdomo and T. Zrnic · 2021
Later among the works it cites.
“The deep bootstrap framework: Good online learners are good offline generalizers”
P. Nakkiran, B. Neyshabur and H. Sedghi · 2021
Later among the works it cites.
“Learning transferable visual models from natural language supervision”
A. Radford, J.. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger and I. Sutskever · 2021
Later among the works it cites.
“Scaling laws for deep learning”, 2021
J.. Rosenfeld · 2021
Later among the works it cites.
“The rich get richer: Disparate impact of semi-supervised learning”
Z. Zhu, T. Luo and Y. Liu · 2021
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“Knowledge distillation: A good teacher is patient and consistent”
L. Beyer, X. Zhai, A. Royer, L. Markeeva, R. Anil and A. Kolesnikov · 2022
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“Performative prediction in a stateful world”
G. Brown, S. Hod and I. Kalemaj · 2022
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“A Systematic Study of Bias Amplification”
M. Hall, L. van Maaten, L. Gustafson and A. Adcock · 2022
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“What You See is What You Get: Distributional Generalization for Algorithm Design in Deep Learning”
B. Kulynych, Y. Yang, Y. Yu, J. Błasiok and P. Nakkiran · 2022
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“Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding”
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. Denton, S… Ghasemipour, B.. Ayan, S.. Mahdavi, R.. Lopes, T. Salimans, J. Ho, D.. Fleet and M. Norouzi · 2022
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“Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection”
M. Sap, S. Swayamdipta, L. Vianna, X. Zhou, Y. Choi and N.. Smith · 2022
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