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With the rise in interest of sparse neural networks, we study how neural network pruning with synthetic data leads to sparse networks with unique training properties.
The information bottleneck method, 2000
Naftali Tishby, Fernando C. Pereira, and William Bialek · 2000
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An intuitive proof of the data processing inequality, 2012
Normand J. Beaudry and Renato Renner · 2012
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Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, and Michael Carbin · 2020
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George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A. Efros, and Jun-Yan Zhu · 2022
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Dataset condensation via efficient synthetic-data parameterization, 2022
Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh, Sangdoo Yun, Hwanjun Song, Joonhyun Jeong, Jung-Woo Ha, and Hyun Oh Song · 2022
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Lottery tickets on a data diet: Finding initializations with sparse trainable networks, 2022
Mansheej Paul, Brett W. Larsen, Surya Ganguli, Jonathan Frankle, and Gintare Karolina Dziugaite · 2022
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Distilled pruning: Using synthetic data to win the lottery, 2023
Luke McDermott and Daniel Cummings · 2023
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Data distillation: A survey, 2023
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Hidenori Tanaka, Daniel Kunin, Daniel L. K. Yamins, and Surya Ganguli · 2020
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Pruning neural networks at initialization: Why are we missing the mark?, 2021
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, and Michael Carbin · 2021
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Noveen Sachdeva and Julian McAuley · 2023
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