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Irrespective of the specific definition of fairness in a machine learning application, pruning the underlying model affects it.
Handwritten digit recognition with a Back-Propagation network
Yann LeCun, Bernhard E Boser, John S Denker, Donnie Henderson, R E Howard, Wayne E Hubbard, and Lawrence D Jackel · 1990
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The MNIST database of handwritten digits
Yann LeCun, Corinna Cortes, and Christopher J. C. Burges · 1994
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Nist special database 19 handprinted forms and characters database
Patrick J Grother · 1995
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Face recognition vendor test 2002: Evaluation report
P J Phillips, Patrick J Grother, Ross J Micheals, D M Blackburn, Elham Tabassi, and Mike Bone · 2003
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Learning multiple layers of features from tiny images
Alex Krizhevsky et al · 2009
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Report on the evaluation of 2D still-image face recognition algorithms
Patrick J Grother, Patrick J Grother, P Jonathon Phillips, and George W Quinn · 2011
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
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Face recognition performance: Role of demographic information
B. F. Klare, M. J. Burge, J. C. Klontz, R. W. Vorder Bruegge, and A. K. Jain · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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Very deep convolutional networks for large-scale image recognition, 2014
Karen Simonyan and Andrew Zisserman · 2014
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Deep residual learning for image recognition, 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
On the (im)possibility of fairness, 2016
Sorelle A. Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian · 2016
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Pruning filters for efficient convnets, 2016
Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet, and Hans Peter Graf · 2016
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EMNIST: an extension of mnist to handwritten letters, 2017
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and André van Schaik · 2017
Cited alongside, same era.
Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data
Michael Veale and Reuben Binns · 2017
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Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms, 2017
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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Why is my classifier discriminatory?, 2018
Irene Chen, Fredrik D. Johansson, and David Sontag · 2018
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Deep learning for classical Japanese literature, 2018
Tarin Clanuwat, Mikel Bober-Irizar, Asanobu Kitamoto, Alex Lamb, Kazuaki Yamamoto, and David Ha · 2018
Model cards for model reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru · 2019
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One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers, 2019
Ari S. Morcos, Haonan Yu, Michela Paganini, and Yuandong Tian · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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ABOUT ML: Annotation and benchmarking on understanding and transparency of machine learning lifecycles, 2019
Inioluwa Deborah Raji and Jingying Yang · 2019
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On the legal compatibility of fairness definitions, 2019
Alice Xiang and Inioluwa Deborah Raji · 2019
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Cited alongside, same era.
The measure and mismeasure of fairness: A critical review of fair machine learning, 2018
Sam Corbett-Davies and Sharad Goel · 2018
Cited alongside, same era.
The lottery ticket hypothesis: Finding sparse, trainable neural networks, 2018
Jonathan Frankle and Michael Carbin · 2018
Cited alongside, same era.
Prediction-based decisions and fairness: A catalogue of choices, assumptions, and definitions, 2018
Shira Mitchell, Eric Potash, Solon Barocas, Alexander D’Amour, and Kristian Lum · 2018
Cited alongside, same era.
Fairness definitions explained
Sahil Verma and Julia Rubin · 2018
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Does object recognition work for everyone?, 2019
Terrance DeVries, Ishan Misra, Changhan Wang, and Laurens van der Maaten · 2019
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The state of sparsity in deep neural networks
Trevor Gale, Erich Elsen, and Sara Hooker · 2019
Cited alongside, same era.
Cold case: The lost MNIST digits, 2019
Chhavi Yadav and Léon Bottou · 2019
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Deconstructing lottery tickets: Zeros, signs, and the supermask, 2019
Hattie Zhou, Janice Lan, Rosanne Liu, and Jason Yosinski · 2019
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What is the state of neural network pruning?, 2020
Davis Blalock, Jose Javier Gonzalez Ortiz, Jonathan Frankle, and John Guttag · 2020
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facebookresearch/qmnist/readme.md
Léon Bottou · 2020
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The early phase of neural network training, 2020
Jonathan Frankle, David J. Schwab, and Ari S. Morcos · 2020
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Streamlining tensor and network pruning in PyTorch, 2020
Michela Paganini and Jessica Forde · 2020
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Comparing rewinding and fine-tuning in neural network pruning, 2020
Alex Renda, Jonathan Frankle, and Michael Carbin · 2020
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